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Nerfies: Deformable Neural Radiance Fields

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- - Keunhong Park1, - - Utkarsh Sinha2, - - Jonathan T. Barron2, - - - Sofien Bouaziz2, - - - Dan B Goldman2, - - - Steven M. Seitz1,2, - - - Ricardo Martin-Brualla2 - -
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ViLaBench

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Benchmark collection for Vision-Language Models (VLMs), hosted by the AntResearchNLP team.

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+ These benchmark and result data are carefully compiled and merged from technical reports and official blogs of renowned multimodal models, including Google's Gemini series, OpenAI GPT-series and OpenAI o-series, Seed1.5-VL, MiMo-VL, Kimi-VL, Qwen2.5-VL, InternVL3, and other leading models' official technical documentation. +

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+ This collection provides researchers and developers with a comprehensive, standardized multimodal model evaluation benchmark comparison platform, helping to advance the development and research in the vision-language model field. Through unified data formats and visualization interfaces, users can more intuitively understand the performance of different models on various tasks, providing valuable references for model selection and improvement. Welcome to submit new benchmarks and results on GitHub! +

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+ Table Headers Explanation +
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  • Benchmark: The name of the vision-language benchmark. Click to visit the official page. 🔗
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  • Year: The year the benchmark was published or released.
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    • Understanding - Basic comprehension and recognition tasks
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    • Comprehensive - Involving both basic understanding and advanced reasoning tasks
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  • Domain: The application domain or context (e.g., natural/synthetic images, chart, etc.).
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  • Modalities: The input data type(s) required (e.g., Single-Image, Multi-Image, Video).
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  • Score: Model performance scores on the benchmark. Click the chart to zoom in for more details.
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- Nerfies turns selfie videos from your phone into - free-viewpoint - portraits. -

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Abstract

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- We present the first method capable of photorealistically reconstructing a non-rigidly - deforming scene using photos/videos captured casually from mobile phones. -

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- Our approach augments neural radiance fields - (NeRF) by optimizing an - additional continuous volumetric deformation field that warps each observed point into a - canonical 5D NeRF. - We observe that these NeRF-like deformation fields are prone to local minima, and - propose a coarse-to-fine optimization method for coordinate-based models that allows for - more robust optimization. - By adapting principles from geometry processing and physical simulation to NeRF-like - models, we propose an elastic regularization of the deformation field that further - improves robustness. -

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- We show that Nerfies can turn casually captured selfie - photos/videos into deformable NeRF - models that allow for photorealistic renderings of the subject from arbitrary - viewpoints, which we dub "nerfies". We evaluate our method by collecting data - using a - rig with two mobile phones that take time-synchronized photos, yielding train/validation - images of the same pose at different viewpoints. We show that our method faithfully - reconstructs non-rigidly deforming scenes and reproduces unseen views with high - fidelity. -

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Video

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Visual Effects

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- Using nerfies you can create fun visual effects. This Dolly zoom effect - would be impossible without nerfies since it would require going through a wall. -

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Matting

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- As a byproduct of our method, we can also solve the matting problem by ignoring - samples that fall outside of a bounding box during rendering. -

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Animation

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Interpolating states

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- We can also animate the scene by interpolating the deformation latent codes of two input - frames. Use the slider here to linearly interpolate between the left frame and the right - frame. -

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- Interpolate start reference image. -

Start Frame

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End Frame

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Re-rendering the input video

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- Using Nerfies, you can re-render a video from a novel - viewpoint such as a stabilized camera by playing back the training deformations. -

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Related Links

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- There's a lot of excellent work that was introduced around the same time as ours. -

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- Progressive Encoding for Neural Optimization introduces an idea similar to our windowed position encoding for coarse-to-fine optimization. -

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- D-NeRF and NR-NeRF - both use deformation fields to model non-rigid scenes. -

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- Some works model videos with a NeRF by directly modulating the density, such as Video-NeRF, NSFF, and DyNeRF -

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- There are probably many more by the time you are reading this. Check out Frank Dellart's survey on recent NeRF papers, and Yen-Chen Lin's curated list of NeRF papers. -

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BibTeX

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@article{park2021nerfies,
-  author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
-  title     = {Nerfies: Deformable Neural Radiance Fields},
-  journal   = {ICCV},
-  year      = {2021},
-}
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