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SPARK can create high-quality 3D face avatars from regular videos and track expressions and poses in real time. It improves the accuracy of 3D face reconstructions for tasks like aging, face swapping, and digital makeup. 6 examples:

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Dreaming Tulpa ๐Ÿฅ“๐Ÿ‘‘

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(1/2) Check out ๐†๐š๐ฎ๐ฌ๐ฌ๐ข๐š๐ง๐€๐ฏ๐š๐ญ๐š๐ซ๐ฌ: Photorealistic Head Avatars with Rigged 3D Gaussians! We create photorealistic head avatars by animating 3D Gaussians on a parametric face model - edited and rendered in *real-time*!
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(1/2) Check out ๐†๐š๐ฎ๐ฌ๐ฌ๐ข๐š๐ง๐€๐ฏ๐š๐ญ๐š๐ซ๐ฌ: Photorealistic Head Avatars with Rigged 3D Gaussians! We create photorealistic head avatars by animating 3D Gaussians on a parametric face model - edited and rendered in *real-time*!

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This is RICULOUSLY good, TRELLIS 3D Generation model by Microsoft! ๐Ÿ”ฅ Generate high-quality 3D assets from text or image prompts. Supports various formats like Radiance Fields, 3D Gaussians, and meshes Available for FREE on Hugging Face!
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This is RICULOUSLY good, TRELLIS 3D Generation model by Microsoft! ๐Ÿ”ฅ Generate high-quality 3D assets from text or image prompts. Supports various formats like Radiance Fields, 3D Gaussians, and meshes Available for FREE on Hugging Face!

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ViewCrafter can generate high-quality 3D views from single or few images using a video diffusion model! It allows for precise camera control and is useful for real-time rendering and turning text into 3D scenes. Links โฌ‡๏ธ
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ViewCrafter can generate high-quality 3D views from single or few images using a video diffusion model! It allows for precise camera control and is useful for real-time rendering and turning text into 3D scenes. Links โฌ‡๏ธ

Dreaming Tulpa ๐Ÿฅ“๐Ÿ‘‘

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NVIDIA introduces Edify 3D AI model You can generate high quality 3D assets from text or image input under 2 minutes! With PBR materials, quad-mesh topologies, and 4k textures. 6 wild examples:
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NVIDIA introduces Edify 3D AI model You can generate high quality 3D assets from text or image input under 2 minutes! With PBR materials, quad-mesh topologies, and 4k textures. 6 wild examples:

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3D AI is getting crazier.. you now can transform character from real life video to 3D model with motion and.. it can also capture the expressions link in comment.
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3D AI is getting crazier.. you now can transform character from real life video to 3D model with motion and.. it can also capture the expressions link in comment.

el.cine

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Stadium-scale, studio-quality motion capture is launching soonโ€ฆ Full 3D control, stable bone lengths and realistic ground-foot contact for high-fidelity animation in Unreal Engine, Blender or any 3D engine. This is not pose estimationโ€”this is dynamic, high-precision, real-world data capturing at AAA quality, providing you with lifelike avatars and seamless animation like never before. ๐Ÿ“ทโšฝ๏ธ
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Stadium-scale, studio-quality motion capture is launching soonโ€ฆ Full 3D control, stable bone lengths and realistic ground-foot contact for high-fidelity animation in Unreal Engine, Blender or any 3D engine. This is not pose estimationโ€”this is dynamic, high-precision, real-world data capturing at AAA quality, providing you with lifelike avatars and seamless animation like never before. ๐Ÿ“ทโšฝ๏ธ

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๐Ÿ“ขAnnouncing our 3D head avatar benchmark๐Ÿ“ข Two tasks with hidden test sets: - Dynamic Novel View Synthesis on Heads - Monocular FLAME-driven Head Avatar Reconstruction Our goal is to make research on 3D head avatars more comparable and ultimately increase the realism of digital humans. The benchmark studies distinct phenomena of 3D head avatar creation, such as extreme facial expressions, slow motion captures of shaking long hair, or complicated light reflection and refraction patterns of glasses. The two benchmark tasks assess two core desiderata of 3D avatars: While the novel view synthesis challenge focuses on best possible rendering quality of complex moving scenes, the avatar animation challenge is concerned with how well a driving signal is translated into an avatar. Evaluations are light-weight and consist of diverse video recordings from the popular NeRSemble dataset with a hidden test set. Participation in the benchmark is therefore straight-forward and requires only 5 reconstructions per task. Leaderboard and benchmark submission: Benchmark data access and toolkit: Great work by Tobias Kirschstein Simon Giebenhain
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๐Ÿ“ขAnnouncing our 3D head avatar benchmark๐Ÿ“ข Two tasks with hidden test sets: - Dynamic Novel View Synthesis on Heads - Monocular FLAME-driven Head Avatar Reconstruction Our goal is to make research on 3D head avatars more comparable and ultimately increase the realism of digital humans. The benchmark studies distinct phenomena of 3D head avatar creation, such as extreme facial expressions, slow motion captures of shaking long hair, or complicated light reflection and refraction patterns of glasses. The two benchmark tasks assess two core desiderata of 3D avatars: While the novel view synthesis challenge focuses on best possible rendering quality of complex moving scenes, the avatar animation challenge is concerned with how well a driving signal is translated into an avatar. Evaluations are light-weight and consist of diverse video recordings from the popular NeRSemble dataset with a hidden test set. Participation in the benchmark is therefore straight-forward and requires only 5 reconstructions per task. Leaderboard and benchmark submission: Benchmark data access and toolkit: Great work by Tobias Kirschstein Simon Giebenhain

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As announced in partnership with NVIDIA at CES, weโ€™re excited to introduce Stable Point Aware 3D (SPAR3D), setting a new standard in 3D generation. Ideal for running on NVIDIA RTX AI PCs, SPAR3D enables real-time editing and complete structure generation of 3D objects from a single image in under a second. You can download the weights on Hugging Face and code on GitHub, or access the model through the Stability AI API. Learn more here: (1/3)
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As announced in partnership with NVIDIA at CES, weโ€™re excited to introduce Stable Point Aware 3D (SPAR3D), setting a new standard in 3D generation. Ideal for running on NVIDIA RTX AI PCs, SPAR3D enables real-time editing and complete structure generation of 3D objects from a single image in under a second. You can download the weights on Hugging Face and code on GitHub, or access the model through the Stability AI API. Learn more here: (1/3)

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๐Ÿ“ขPix2NPHM: Learning to Regress NPHM Reconstructions From a Single Image๐Ÿ“ข We directly regress neural parametric head models (NPHMs) from a single image โ€” fast, stable, and significantly more expressive than classical 3DMMs such as FLAME. Face tracking & 3D reconstruction are often limited by the representational capacity of PCA-based face models. By lifting NPHMs to a first-class reconstruction primitive, we enable more accurate geometry, richer expressions, and finer animation control. Pix2NPHM obtains fast and reliable NPHM reconstructions on real-world data. Inference-time optimization against surface normals and canonical point maps can further increase fidelity. Key to successful and generalized training of our ViT-based network are: (1) large-scale registration of existing 3D head datasets, and (2) self-supervised training on vast in-the-wild 2D video datasets using pseudo ground-truth surface normals. Finally, we show that geometry-aware pretraining on pixel-aligned reconstruction tasks significantly outperforms generic visual pretraining (e.g., DINO-style features) in terms of generalization. ๐ŸŒ ๐ŸŽฅ Great work by Simon Giebenhain, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Zhe Chen
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Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians paper page: Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups. In this paper, we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other, thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore, we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions.

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this is the future of CGI this new OpenMagic AI can add image, video or 3D product into real life video seamlessly with one click and.. it can even turn product image into 3D model automatically 10 wild examples:

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Bad at drawing different types of faces? With Clip Studio Paint's 3D head model, you can adjust parts like the face shape and eyes, or even change the angle. It's a great help when you want to draw a face shape you're unfamiliar with, or when you want to draw the same face repeatedly. For more information here โ†“ #clipstudio

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Introducing ๐Ÿ“ฆ๐—”๐—ฟ๐˜๐—ถ๐—Ÿ๐—ฎ๐˜๐—ฒ๐—ป๐˜๐Ÿ”ง (SIGGRAPH Asia 2025) โ€” a high-quality 3D diffusion model that explicitly models object articulation, paving the way for richer, more realistic assets in embodied AI and simulation: โ€“ Generates fully articulated 3D objects โ€“ Physically plausible joints & motion โ€“ High-fidelity 3D Gaussian appearance โ€“ Supports generation from a single real image arXiv: Project: Code (coming soon):
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Introducing ๐Ÿ“ฆ๐—”๐—ฟ๐˜๐—ถ๐—Ÿ๐—ฎ๐˜๐—ฒ๐—ป๐˜๐Ÿ”ง (SIGGRAPH Asia 2025) โ€” a high-quality 3D diffusion model that explicitly models object articulation, paving the way for richer, more realistic assets in embodied AI and simulation: โ€“ Generates fully articulated 3D objects โ€“ Physically plausible joints & motion โ€“ High-fidelity 3D Gaussian appearance โ€“ Supports generation from a single real image arXiv: Project: Code (coming soon):

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