REVIEW 9 cited by
Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
read the original abstract
In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques. The methodology utilizes the SMPL(Skinned Multi-Person Linear) model as the 3D human parametric model to establish a unified representation of body shape and pose. This facilitates the accurate capture of intricate human geometry and motion characteristics from source videos. Specifically, we incorporate rendered depth images, normal maps, and semantic maps obtained from SMPL sequences, alongside skeleton-based motion guidance, to enrich the conditions to the latent diffusion model with comprehensive 3D shape and detailed pose attributes. A multi-layer motion fusion module, integrating self-attention mechanisms, is employed to fuse the shape and motion latent representations in the spatial domain. By representing the 3D human parametric model as the motion guidance, we can perform parametric shape alignment of the human body between the reference image and the source video motion. Experimental evaluations conducted on benchmark datasets demonstrate the methodology's superior ability to generate high-quality human animations that accurately capture both pose and shape variations. Furthermore, our approach also exhibits superior generalization capabilities on the proposed in-the-wild dataset. Project page: https://fudan-generative-vision.github.io/champ.
Forward citations
Cited by 9 Pith papers
-
ExpertEdit: Learning Skill-Aware Motion Editing from Expert Videos
ExpertEdit edits novice motions to expert skill levels by learning a motion prior from unpaired videos and infilling masked skill-critical spans.
-
HandsOnWorld: Unconstrained Egocentric Video Generation with Camera-Disentangled Hand Control
HandsOnWorld creates a hand-controlled egocentric video generator from unconstrained monocular video via a new EgoVid-Pro dataset from monocular reconstruction and a Plücker Hand Map that disentangles camera and hand motion.
-
Beyond Skeletons: Learning Animation Directly from Driving Videos with Same2X Training Strategy
DirectAnimator bypasses pose extraction using a Driving Cue Triplet and Same2X training strategy to achieve state-of-the-art human animation quality and robustness from raw videos.
-
FreeAnimate: Training-Free Human Image Animation with Preview-Guided Denoising
A training-free framework for human image animation that uses preview-guided denoising plus Inversion-Boosted and Reference-Anchored Self-Attention modules to achieve temporal consistency and identity preservation in ...
-
Stylistic Attribute Control in Latent Diffusion Models
A technique for parametric stylistic control in latent diffusion models learns disentangled directions from synthetic datasets and applies them via guidance composition while preserving semantics.
-
HairWeaver: Few-Shot Photorealistic Hair Motion Synthesis with Sim-to-Real Guided Video Diffusion
HairWeaver animates a single human photo with physically plausible hair motion by transferring simulated CG hair dynamics into a frozen video diffusion model via two lightweight LoRA adapters.
-
Multimodal Diffusion Transformer with Memory Bank for Scalable Long-Duration Talking Video Generation
LetsTalk combines a multimodal diffusion transformer, noise-regularized memory bank, deep compression autoencoder, and symbiotic/direct fusion schemes to achieve state-of-the-art quality and efficiency in long-duratio...
-
Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification
Pose-dIVE augments Re-ID training sets with diffusion-generated images of diverse poses and viewpoints by conditioning on SMPL parameters.
-
LUNA: Learning Universal 3D Human Animation Beyond Skinning
LUNA is an LBS-free neural animation model that maps 2D controls to 3D Gaussian deformations via a transformer motion regressor and hybrid supervision for realistic motion and zero-shot generalization.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.