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AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models

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arxiv 2505.20255 v2 pith:AGTAMJ3W submitted 2025-05-26 cs.CV

AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models

Muyao Niu , Mingdeng Cao , Yifan Zhan , Qingtian Zhu , Mingze Ma , Jiancheng Zhao , Yanhong Zeng , Zhihang Zhong
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Xiao Sun Yinqiang Zheng
This is my paper
classification cs.CV
keywords animationanicraftercharacterdiffusionhuman-centricopen-domainanimateavatar-background
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Recent advances in video diffusion models have significantly improved character animation techniques. However, current approaches rely on basic structural conditions such as DWPose or SMPL-X to animate character images, limiting their effectiveness in open-domain scenarios with dynamic backgrounds or challenging human poses. In this paper, we introduce \textbf{AniCrafter}, a diffusion-based human-centric animation model that can seamlessly integrate and animate a given character into open-domain dynamic backgrounds while following given human motion sequences. Built on cutting-edge Image-to-Video (I2V) diffusion architectures, our model incorporates an innovative ''avatar-background'' conditioning mechanism that reframes open-domain human-centric animation as a restoration task, enabling more stable and versatile animation outputs. Experimental results demonstrate the superior performance of our method. Codes are available at https://github.com/MyNiuuu/AniCrafter.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos

    cs.CV 2026-01 unverdicted novelty 7.0

    CoMoVi co-generates 3D human motions and 2D videos synchronously in a single diffusion denoising loop using 3D-to-2D projection and dual-branch diffusion with 3D-2D cross attentions.

  2. 4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans

    cs.CV 2026-07 conditional novelty 6.0

    A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.

  3. 3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement

    cs.CV 2026-06 unverdicted novelty 6.0

    Presents a scene-adaptive 3D human animation method using ground-adaptive motion retargeting and viewpoint-adaptive latent fusion to control human trajectories and camera views, reporting gains on two benchmarks.

  4. 3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement

    cs.CV 2026-06 conditional novelty 6.0

    A training-free 3D scene-adaptive human animation framework that controls human motion and camera trajectories via ground-adaptive retargeting and visibility-masked point-cloud fusion in a diffusion backbone.

  5. 3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement

    cs.CV 2026-06 unverdicted novelty 5.0

    Presents a scene-adaptive 3D human image animation framework using ground-adaptive motion retargeting and viewpoint-adaptive latent fusion to control human and camera trajectories, claiming improvements on two benchmarks.

  6. Proxy-GS: Unified Occlusion Priors for Training and Inference in Structured 3D Gaussian Splatting

    cs.CV 2025-09 conditional novelty 5.0

    A proxy mesh rendered through hardware rasterization provides a cheap occlusion depth prior that culls hidden anchors at inference and guides densification at training, giving Octree-GS-like MLP splatting a 3 to 4x sp...