HumANDiff improves motion consistency in human video generation by sampling diffusion noise on an articulated human body template and adding joint appearance-motion prediction plus a geometric consistency loss.
LHM: large animatable human reconstruction model from a single image in seconds
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 9years
2026 9representative citing papers
Pretraining on 1M wild videos followed by post-training on curated data yields high-fidelity feedforward 3D avatars that generalize across identities, clothing, and lighting with emergent relightability and loose-garment support.
VRGaussianAvatar enables real-time full-body 3D Gaussian Splatting avatars in VR from HMD tracking alone via inverse kinematics and binocular batching for efficient stereo rendering, outperforming mesh baselines in performance and user ratings.
PointSplat infers compact Gaussian splats directly in 3D space from input point sets via ray casting and Point-Image Transformer to reduce inter-view redundancy and improve novel-view quality for humans.
A 4D try-on proxy (3DGS avatar + SMPL-X + background points) anchors a DiT so virtual try-on videos can follow arbitrary camera trajectories with consistent garments and scene structure.
A single-image head reconstruction method uses coarse-to-fine optimization with normal consistency, landmarks, and geometry-aware constraints on curvature and conformality to produce meshes with industry-grade topology and preserved facial identity.
A coupled world-agent framework uses 3D Gaussian reconstruction and first-person RGB-D perception with iterative planning to enable goal-directed, collision-avoiding humanoid behavior in novel reconstructed scenes.
TrioMan is a tri-module data augmentation framework using a Generator for pose/camera perturbations, a Refiner with one-step diffusion, and an Examiner with dual-branch attention to improve 3D avatar learning from monocular videos, claiming better results than prior methods on two benchmarks.
HUG3D uses group-instance multi-view diffusion and physics-based optimization to create physically plausible 3D reconstructions of interacting people from a single image.
citing papers explorer
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HumANDiff: Articulated Noise Diffusion for Motion-Consistent Human Video Generation
HumANDiff improves motion consistency in human video generation by sampling diffusion noise on an articulated human body template and adding joint appearance-motion prediction plus a geometric consistency loss.
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Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining
Pretraining on 1M wild videos followed by post-training on curated data yields high-fidelity feedforward 3D avatars that generalize across identities, clothing, and lighting with emergent relightability and loose-garment support.
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VRGaussianAvatar: Integrating 3D Gaussian Avatars into VR
VRGaussianAvatar enables real-time full-body 3D Gaussian Splatting avatars in VR from HMD tracking alone via inverse kinematics and binocular batching for efficient stereo rendering, outperforming mesh baselines in performance and user ratings.
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PointSplat: Compact Gaussian Splatting via Human-Centric Prediction
PointSplat infers compact Gaussian splats directly in 3D space from input point sets via ray casting and Point-Image Transformer to reduce inter-view redundancy and improve novel-view quality for humans.
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TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy
A 4D try-on proxy (3DGS avatar + SMPL-X + background points) anchors a DiT so virtual try-on videos can follow arbitrary camera trajectories with consistent garments and scene structure.
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High-Fidelity Single-Image Head Modeling with Industry-Grade Topology
A single-image head reconstruction method uses coarse-to-fine optimization with normal consistency, landmarks, and geometry-aware constraints on curvature and conformality to produce meshes with industry-grade topology and preserved facial identity.
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Visually-grounded Humanoid Agents
A coupled world-agent framework uses 3D Gaussian reconstruction and first-person RGB-D perception with iterative planning to enable goal-directed, collision-avoiding humanoid behavior in novel reconstructed scenes.
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Generator-Refiner-Examiner: A Tri-Module Data Augmentation Framework for 3D Human Avatar Learning from Monocular Videos
TrioMan is a tri-module data augmentation framework using a Generator for pose/camera perturbations, a Refiner with one-step diffusion, and an Examiner with dual-branch attention to improve 3D avatar learning from monocular videos, claiming better results than prior methods on two benchmarks.
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Human Interaction-Aware 3D Reconstruction from a Single Image
HUG3D uses group-instance multi-view diffusion and physics-based optimization to create physically plausible 3D reconstructions of interacting people from a single image.