The proposed model can handle the detailed local facial semantics and the global head-torso relationship through two semantic-aware modules. Gafni et al . Training DeepSDF for a specific mesh was done with 300500K points, and that caused the training to be computationally expensive. arXiv preprint arXiv:2104.14557 (2021), Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. Image Process. https://doi.org/10.1007/978-3-030-58577-8_25, Deng, K., Liu, A., Zhu, J.Y., Ramanan, D.: Depth-supervised NeRF: fewer views and faster training for free. Computer graphics tackles the problem of creating images with a computer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), vol. Luckily, at NeurIPS 2021, two interesting papers tackled that specific problem. : Expressive talking head generation with granular audio-visual control. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11217, pp. I will also briefly present the KITTI-360 dataset, a new outdoor dataset with 360 degree sensor information and semantic annotations in 3D and 2D which will be released this summer. Instead of mapping a coordinate to latent features, and using the complex mechanism of SRN to generate the corresponding pixel colors, NeRF directly regresses the RGB-alpha value for that coordinate and feeds it into a differentiable ray-marching renderer. We develop an implicit neural representation for videos based on these observations. Since we are summing light, it is crucial that we can weigh how much the light coming from some position along the ray coordinate affects the total light? concerning the scene properties (occlusions, material, etc.). Abstract: In the deep learning era, long video generation of high-quality still remains challenging due to the spatio-temporal complexity and continuity of videos. Please refer to DeepSDF Section 4 for more details. And thats the crucial rule that predicting the density (sigma) value has in the NeRF algorithm. For a given a specific pixel, its color is affected by multiple 3D coordinates along the ray reaching the camera and intersecting that pixel. The ray from the camera through the pixel can be seen in the figure below. Videos are up-sampled in space and time simultaneously. 405421. A common theme in analyzing learning problems is to split the task into two different sub-tasks. ACCV (2016), Chung, J.S., Jamaludin, A., Zisserman, A.: You said that? "NB" means Neural Body. Similarly, implicit representations (not necessarily learned) such as SDFs and Riemannian Motion Policies (RMPs) have been used with great success in robotic motion planning, manipulation, and perception. It enables free-view control with higher image quality compared to explicit methods, which is suitable for the video portrait generation task. We compare VideoINR with the raw pixels and The key idea behind InstantNGP is to capture the coarse and fine details by several grids of different sizes. Recent works have shown the ability of Implicit Neural Representations (INR) to carry meaningful representations of signal derivatives. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. However, to solve the novel view synthesis task, a scene representation is needed. arXiv preprint arXiv:2105.02872 (2021), Peng, S., et al. Therefore, SRN suggested a somewhat complex temporal ray marching layer (called ray-marching LSTM) to solve the sampling problem. incorporate temporal interpolation and spatial super-resolution in a unified framework. To solve this ill-posed problem, our key idea is to integrate observations over video frames. Neural Body can reconstruct a moving human from a monocular video. At decoding time, the transmitted MLP is evaluated at all pixel locations to reconstruct the image. Date: April 14, 2021. Their method goes as follows: This process is summarized in the following figure: InstantNGP multi-resolution hash-grid demonstration in 2D. In: ICCV (2019), Zhang, J.Y., Yang, G., Tulsiani, S., Ramanan, D.: NeRS: neural reflectance surfaces for sparse-view 3D reconstruction in the wild. The learned implicit neural representation can be decoded to videos of IEEE Trans. (eds.) 2021 - 2022-How can we understand scenes with NNs? arXiv preprint arXiv:2107.02791 (2021), Fisher, C.G. (eds.) A pose-driven deformation based on the linear blend skinning algorithm, which combines the blend weight and the 3D human skeleton to produce observation-to-canonical correspondences, which outperforms recent human modeling methods. Our results indicate that prediction in pretrained neural language models is supported, at least in part, by dynamic representations of meaning and implicit simulation of entity state, and that this behavior can be learned with only text as training data. With this in mind, I see the DeepLSs grid as a local feature extractor and the neural network as a simple regression model. 41764186 (2021), Zhou, Y., Han, X., Shechtman, E., Echevarria, J., Kalogerakis, E., Li, D.: MakeltTalk: speaker-aware talking-head animation. Another common way to represent 3D objects is using continuous implicit representations. We achieve state of the . For a given point, x, find the indices of the corners of the containing voxels at each resolution level. Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video. While it might sound simple, it is certainly a complex task to create physically correct and realistic images. "NeRF-NBW", "NeRF-PDF" and "SDF-PDF" are our animatable human representations. Figure from NeRF paper. Traditionally, the feature extraction part does the heavy lifting of transforming raw data into good representation features that enables the regression (down-stream task, in general) to be simple. We show that VideoINR achieves competitive performances with However, as shown by DeepSDF, their network architecture was chosen after considering the speed vs accuracy tradeoff, as they found that a smaller model performs worse. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. Given a frame index, NeRV outputs the corresponding RGB image. How one should encode this information to SRN was unclear. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. ACM Trans. Recall that an intuitive continuous geometry representation for deep learning is the SDF. A low-resolution grid takes only a small amount of memory and captures coarse details only. arXiv preprint arXiv:2001.05201 (2020), Song, Y., Zhu, J., Li, D., Wang, X., Qi, H.: Talking face generation by conditional recurrent adversarial network. 86498658, June 2021, Graham, B., Engelcke, M., Van Der Maaten, L.: 3D semantic segmentation with submanifold sparse convolutional networks. In recent years there is an explosion of neural implicit representations that helps solve computer graphic tasks. However, most of Wait, how can we train a neural network to represent the scene? 8088 (2017), Prajwal, K., Mukhopadhyay, R., Namboodiri, V.P., Jawahar, C.: A lip sync expert is all you need for speech to lip generation in the wild. COIN: COmpression with Implicit Neural representations. Each of those requirements encompasses many challenges. (eds.) Generally, an implicit function represents a geometry as a function that operates on a 3D point that satisfies: Specifically, the Signed Distance Function (SDF) satisfies those properties. The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate. arXiv preprint arXiv:1803.09179 (2018), Rossler, A., et al. Specifically, training each one of them for a specific scene takes ~1214 hours. A high-resolution grid may capture fine details, at the cost of tremendous memory usage. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1031810327 (2021), Raj, A., et al. It means that we need it to generate images from specific views we have on the scene and train it to minimize the difference between the generated image and the true one. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. Demo video and more resources can be found in https://alvinliu0.github.io/projects/SSP-NeRF. How can one obtain an efficient and compact representation while capturing high-frequency, local detail is a challenging task. If you are not familiar with those terms, I refer you to read them before following on. 86288638 (2021), Chen, Y., Wu, Q., Zheng, C., Cham, T.J., Cai, J.: Sem2NeRF: converting single-view semantic masks to neural radiance fields. This nature enables extending the latent interpolation space of Space-Time Video Super Resolution (STVSR) Recently, the implicit 3D scene representation of Neural Radiance Fields (NeRF) [ 34] provides a new perspective for realistic generation. Recall that DeepLS made the sampling at DeepSDF faster by incorporating a learnable voxel grid. Recall that the general goal of computer graphics is to create images using computers. Yet, while NeRF can generate novel views of the scene, it is not clear how to extract the geometry. 2The Chinese University of Hong Kong Here, Ill describe ways to extract a geometry (surface) out of it. The figure is taken from DeepLS. However, since there are infinite 3D coordinates along that ray, and the scene geometry implicitly emerges during training, SRN had to deal with a critical problem- how to decide what points to sample along the ray? arXiv preprint arXiv:2002.10137 (2020), Zakharov, E., Shysheya, A., Burkov, E., Lempitsky, V.: Few-shot adversarial learning of realistic neural talking head models. It is reasonable to think that if a computer can generate novel views of a scene, it had to (implicitly) reconstruct the 3D model of the scene. arXiv preprint arXiv:2106.02019 (2021), Liu, S., Zhang, Y., Peng, S., Shi, B., Pollefeys, M., Cui, Z.: DIST: rendering deep implicit signed distance function with differentiable sphere tracing. In this workshop, we seek to explore the future of implicit neural representations (INRs) in robotics. Researchers at Picsart AI Research (PAIR), USTC, UC San Diego, UIUC, UT Austin, and the University of Oregon propose a unique Video Implicit Neural Representation (VideoINR) as a continuous video representation. Keynote presented on June 19, 2020 at CVPR in the2nd ScanNet Indoor Scene Understanding ChallengeSlides: http://www.cvlibs.net/talks/talk_cvpr_2020_implicit_. arXiv preprint arXiv:2207.09686 (2022), Xu, X., Pan, X., Lin, D., Dai, B.: Generative occupancy fields for 3D surface-aware image synthesis. All methods are trained on three views and tested on one view. If implicit representations are new to you, I recommend reading the lecture notes from the Missouri CS8620 course. https://doi.org/10.1007/978-3-031-19836-6_7, https://alvinliu0.github.io/projects/SSP-NeRF, Shipping restrictions may apply, check to see if you are impacted, https://doi.org/10.1007/978-3-030-58545-7_3, https://doi.org/10.1007/978-3-030-01234-2_32, https://doi.org/10.1007/978-3-319-54184-6_6, https://doi.org/10.1007/978-3-030-58577-8_25, https://doi.org/10.1007/978-3-030-58452-8_24, https://doi.org/10.1007/978-3-030-58580-8_31, https://doi.org/10.1007/978-3-030-58517-4_42, https://doi.org/10.1007/978-3-030-58589-1_42, https://doi.org/10.1007/978-3-030-01261-8_41, Tax calculation will be finalised during checkout. Nerfies: Deformable Neural Radiance Fields. Int. In: ICCV (2019), Sitzmann, V., Zollhfer, M., Wetzstein, G.: Scene representation networks: Continuous 3D-structure-aware neural scene representations. : EAMM: one-shot emotional talking face via audio-based emotion-aware motion model. To understand how effective their method is, note that the training time of a NeRF network for a single scene was reduced from ~12 hours to about 5 seconds. Using the 5D coordinate-based MLP, NeRF estimates each pixel color by computing: where \(T_i = exp(-\sum_{j = 1}^{i-1}\sigma_{j}\delta_{j})\). For a given point \(x\) find the corresponding voxel on the grid \(V_i\). In: International Conference on Machine Learning, pp. instead of following the discrete representations, we propose Video Implicit Neural Representation (VideoINR), Figure from MonoSDF. arXiv preprint arXiv:2106.12052 (2021), Yariv, L., et al: Multiview neural surface reconstruction by disentangling geometry and appearance. Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips, Not logged in Please refer to their paper for more details. 12375, pp. : Everybodys talkin: Let me talk as you want. SRN proposed to represent the scene using a neural network. However, if youre not that familiar with rendering, just note that rasterization is much faster on modern GPUs, yet it does not model the physical world as well as ray-tracing-based rendering. Updated on Oct 1. Springer, Cham (2020). Video Implicit Neural Representation (VideoINR) maps any 3D space-time coordinate to an RGB value. https://doi.org/10.1007/978-3-030-58452-8_24, Narvekar, N.D., Karam, L.J. arXiv preprint arXiv:2007.11571 (2020), Liu, L., Habermann, M., Rudnev, V., Sarkar, K., Gu, J., Theobalt, C.: Neural actor: neural free-view synthesis of human actors with pose control. However, the representation learning will be ill-posed if the views are highly sparse. Traditional explicit object representations commonly couple the 3D shape data with . In essence, their method is: A 2D comparison of DeepSDF and DeepLS approaches. We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. However, defining the threshold for high density is far from a trivial task. However, it is not trivial how to obtain the SDF of some shape? Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation. Extensive evaluations demonstrate that our proposed approach renders realistic video portraits. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. Note that DeepLS measured their contribution in several aspects, while I focus only on efficiency. (IPF), a new method for video and image compression based on implicit neural representations (INR) that addresses these practical shortcomings. This goal can be decomposed into several problems. While significant work has improved the geometric fidelity of these representations, much less attention is given to their final appearance. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. The SDF to density transformation must satisfy the following properties, as described by NeuS: Intuitively, the transformation is simply using the logistic distribution centered at zero to map SDF values to density values. Note that \(T_i\) computes the accumulated transmittance (i.e., the probability that a ray travels up to some distance without hitting an opaque particle). While the SRN training mechanism is somewhat complex, the general idea can be intuitively simplified. VideoINR defines continuous representations for videos. 1295912970 (2021), Vougioukas, K., Petridis, S., Pantic, M.: Realistic speech-driven facial animation with GANs. However, as shown by NeuS and VolSDF, more sophisticated approaches are needed to make the transformation unbiased and occlusion-aware. Ideally, this representation will be continuous and enable one to query the scene from different views and control the scene properties (for example, its light sources). https://doi.org/10.1007/978-3-030-58589-1_42, Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: NeuS: learning neural implicit surfaces by volume rendering for multi-view reconstruction. Here I suggest another way to think about their suggestion. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. Part of Springer Nature. 690706. DeepSDF We show that VideoINR achieves competitive performances with state-of-the-art STVSR methods on common up-sampling scales and significantly outperforms prior works on continuous and out-of-training-distribution scales. In recent years there is an explosion of neural implicit representations that helps solve computer graphic tasks. These voxels are expensive in memory, especially when a fine-grained approximation is needed. NeRF But the important thing to note is that volume rendering is basically all about approximating the summed light radiance along the ray reaching the camera. To do so, one needs to understand how to represent 3D scenes, which typically contain light, different materials, several geometries, and the model of a camera that is taking the picture. with arbitrary space-time resolution, we can zoom in on videos and transform them into Their works are dubbed VolSDF and NeuS (read as news). Res. arXiv e-prints pp. Request PDF | Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives | Recently, Implicit Neural Representations (INRs) parameterized by neural networks have . While it is clear that a 3D scene contains several 3D objects, it is not clear how to represent their geometries. : Deep speech 2: end-to-end speech recognition in English and mandarin. 173182. DeepSDF took 8 days to reach DeepLS results after 1 minute. On a continuous representation Surface-Reconstruction In this talk, I will propose a hybrid model that uses both a neural implicit shape representation as well as 2D/3D convolutions for detailed reconstruction of objects and large-scale 3D scenes. Specifically, using advances in the field of monocular geometry prediction (i.e., predicting normal or depth images directly from a single RGB image), it supervises the network to render depth and normal images alongside the reconstructed monocular image for the NeRF training. : HyperNeRF: a higher-dimensional representation for topologically varying neural radiance fields. them only support a fixed up-sampling scale, which limits their flexibility and applications. ECCV 2022: Computer Vision ECCV 2022 Feed the latent code \(z_i\) of the voxel \(V_i\) and the transformed coordinate \(T_i\) to the network and regress the SDF. This enables simultaneous sampling and interpolating of video frames at any frame rate and spatial precision. Concatenate the vectors from each resolution level and feed them into the neural network. The key limiting factor of implicit methods is their simple fully-connected network architecture which does not allow for integrating local information in the observations or incorporating inductive biases such as translational equivariance. In this post, I will focus on works that leverage differential ray-tracing rendering as they tend to produce superior results, and I will describe ways to make them usable for real-time applications. In this work, we leverage this property to perform Video Frame Interpolation (VFI) by explicitly constraining the derivatives of the INR to satisfy the optical ow constraint equation. Transform \(x\) to the voxel \(V_i\) local coordinate system, \(T_i(x) = x-x_i\). 87103. https://doi.org/10.1007/978-3-030-58517-4_42, Tretschk, E., Tewari, A., Golyanik, V., Zollhofer, M., Lassner, C., Theobalt, C.: Non-rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video. 32, pp. LNCS, vol. 23 min We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. It is shown that constraining the INR derivatives not only allows to better interpolate intermediate frames but also improves the ability of narrow networks to fit the observed frames, which suggests potential applications to video compression and INR optimization. This is simply my way to distill the progress into different stages, while obviously, the field did not progress in such a clear way. (eds.) Thats about 10,000x faster. Once we can represent the SDF using a neural network, several questions about this representation arise. LNCS, vol. This implicit neural representation for each query point only depends on the \(k\) refined neighbor points (distant information is gathered by the earlier refinement step), the hidden temporal state \(h^{t-1}(q)\) of the query point \(q\), and a small image feature at \(q\). While generating images using neural networks (so-called generative models, such as the famous StyleGAN) made great success in recent years, it failed to do so in a multi-view consistent manner, and precisely conditioning it on a 3D viewpoint is non-trivial. 12348, pp. This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. In this section, we describe COmpressed Implicit Neural representations (COIN), our proposed method for image compression. lecture notes from the Missouri CS8620 course, using a large frozen (already trained on a massive dataset) neural network, Deep Learning Optimization Theory - Trajectory Analysis of Gradient Descent, From N-grams to CodeX (Part 1-N-grams ->RNN) . arbitrary spatial resolution and frame rate. : Visual sound localization in the wild by cross-modal interference erasing. However, it is crucial to point out an unintuitive issue regarding InstantNGP. Google Scholar, Chen, L., Li, Z., Maddox, R.K., Duan, Z., Xu, C.: Lip movements generation at a glance. slow motion simultaneously, while maintaining high fidelity. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. https://doi.org/10.1007/978-3-030-58580-8_31, Pham, H.X., Cheung, S., Pavlovic, V.: Speech-driven 3D facial animation with implicit emotional awareness: a deep learning approach. 10112, pp. decode a 3D space-time coordinate to a motion flow vector. : Mip-NeRF: a multiscale representation for anti-aliasing neural radiance fields. https://doi.org/10.1007/978-3-030-58545-7_3, CrossRef Specifically, on solving the novel view synthesis task using neural networks, as suggested by SRN and NeRF. Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit representations. ECCV 2020. This is the official implementation of our mesh-based neural network (MESH2IR) to generate acoustic impulse responses (IRs) for indoor 3D scenes represented using a mesh. 20192028 (2020), Liu, X., et al. Repeat 25 for some large number of steps, while optimizing for the latent codes \(z_i\) and the neural network parameters. ArXiv We propose a method to compress full-resolution video sequences with implicit neural representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. However, the important thing to note is that those approaches have inherent tradeoffs regarding efficiency (voxel-based representations memory usage grows cubically with respect to the resolution), expressivity (fine geometry such as hair is notoriously hard to model using meshes), or topological constraints (producing a watertight surface, i.e. We then sample a new (eds.) : FaceForensics: a large-scale video dataset for forgery detection in human faces. However, this order makes the ideas and works Ill cover in this post, in my opinion, easier to grasp. continuous and out-of-training-distribution scales.
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