Source-linked AI summary
Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance
Li Hu, Guangyuan Wang, Zhen Shen, Xin Gao, Dechao Meng, Lian Zhuo, Peng Zhang, Bang Zhang, Liefeng Bo
TL;DR
Existing diffusion-based character animation methods improve consistency and generalizability but fail to maintain reasonable character-environment associations and object interactions. Animate Anyone 2 adds environmental conditioning, shape-agnostic masking, object-feature injection, and pose modulation; experiments report high-fidelity generation, seamless integration, coherent interactions, and robust diverse-motion handling. The method remains limited for small complex hand-object interactions, large shape discrepancies, and segmentation-dependent object interactions.
Problem
Existing character animation methods do not adequately preserve authentic character-environment relationships and human-object interactions when motion and context come from different sources.
Method
Animate Anyone 2 conditions animation on source-video environments, formulates character-excluded regions, injects interactive-object features with spatial blending, and augments skeleton motion with depth-wise pose modulation.
Results
Experiments report high-fidelity generation with seamless scene integration, coherent object interaction, robust diverse-motion handling, and superior character-animation results.
Takeaways & Limitations
The framework extends character image animation toward environment affordance by jointly representing character motion, environmental context, object interactions, and complex poses.
Takeaways & Limitations
The approach may produce artifacts in small complex hand-object interactions, deformation artifacts with substantial character-shape discrepancies, and performance that depends on SAM segmentation.
Abstract
from arXiv · showhide
Recent character image animation methods based on diffusion models, such as Animate Anyone, have made significant progress in generating consistent and generalizable character animations. However, these approaches fail to produce reasonable associations between characters and their environments. To address this limitation, we introduce Animate Anyone 2, aiming to animate characters with environment affordance. Beyond extracting motion signals from source video, we additionally capture environmental representations as conditional inputs. The environment is formulated as the region with the exclusion of characters and our model generates characters to populate these regions while maintaining coherence with the environmental context. We propose a shape-agnostic mask strategy that more effectively characterizes the relationship between character and environment. Furthermore, to enhance the fidelity of object interactions, we leverage an object guider to extract features of interacting objects and employ spatial blending for feature injection. We also introduce a pose modulation strategy that enables the model to handle more diverse motion patterns. Experimental results demonstrate the superior performance of the proposed method.
1. Introduction
Existing character animation methods improve appearance consistency, motion stability, and generalizability but poorly model character-environment relationships and interactions. Animate Anyone 2 addresses this gap by conditioning on source-video environments, injecting interactive-object features, and modulating pose representations.
- Diffusion-based animation methods have improved appearance consistency, motion stability, and character generalizability.
- Existing workflows often combine motion from one video with environments from static images, producing inauthentic spatial relationships and disrupted human-object interactions.
- Animate Anyone 2 defines animation as preserving source-video motion while reproducing character-environment relationships consistent with that video.
- The framework captures environmental representations as conditional inputs, removes character regions to formulate the environment, and learns character-context fusion end to end.
- A shape-agnostic mask, lightweight object guider with spatial blending, and depth-wise pose modulation target boundary fidelity, object interactions, and diverse complex poses.
- The reported advantages are seamless scene integration, coherent object interaction, robust diverse-motion handling, and superior character-animation results on corresponding benchmarks.
2. Related Works
Related work spans diffusion-based motion-control systems, scene-affordance generation, and character animation involving scenes or objects. Animate Anyone 2's framework combines environmental conditioning, object-feature injection, and pose modulation within this broader landscape.
- Diffusion-based image animation has become mainstream, with Animate Anyone using Stable Diffusion, a 3D UNet, and ReferenceNet for temporal and appearance modeling.
- MimicMotion, UniAnimate, DisCo, MagicAnimate, and Champ vary the motion representation or control signal, including skeletons, temporal reference stacking, DensePose, and SMPL-derived modalities.
- Animate Anyone 2's framework captures environmental information, injects interactive-object features through spatial blending, and uses pose modulation for inter-limb relationships.
- Other studies insert or inpaint people into scenes, using video self-supervision, semantic masks, or related scene-affordance formulations.
- Character-animation systems for scene or object interactions include MovieCharacter's compositing, AnchorCrafter's human-object manipulation, and MIMO's decomposition into human, background, and occlusion.
3. Method
Animate Anyone 2 conditions diffusion-based character animation on motion, environment, and object representations to preserve character–context relationships. Its method combines shape-agnostic environment masks, object-feature injection, and depth-wise pose modulation.
- 3.1. Framework: The framework uses self-supervised video reconstruction, separating character, environment, and interactive-object sequences while using motion as driving input.During inference, a target character image and driving video generate animation with corresponding actions and environmental relationships.
- 3.1. Framework: Environmental representations are encoded and merged with noise latents, while motion information is integrated through pose modulation.The denoising network also receives appearance features from the reference character image.
- 3.2. Environment Formulation: The shape-agnostic mask disrupts correspondence between mask boundaries and character outlines, improving context integration without fixed boundary constraints.Patchwise mask reformulation creates shapes that deviate from character boundaries; random scaling further prevents mask size from constraining animation.
- 3.3. Object Injection: The object guider extracts interactive-object features, and spatial blending injects them into decoder stages to preserve character–object interaction details.Objects are localized, segmented, encoded into object latents, and blended with noise latents during denoising.
- 3.4. Pose Modulation: Depth-wise pose modulation augments skeleton signals with structured depth to represent inter-limb spatial relationships while reducing shape leakage.Depth is extracted within a binarized skeleton mask before pose-feature processing.
4. Experiments
Experiments evaluate Animate Anyone 2 on character animation, environment affordance, and ablations of its environment, object, and pose designs. Qualitative and quantitative comparisons report strong character-environment integration and robust handling of diverse motions.
- Qualitative Results: Animate Anyone 2 achieves seamless visual integration and interaction between diverse animated characters and their surrounding environments.Figure 4 presents qualitative results for high-fidelity character animation with environment affordance.
- Comparisons: The method is evaluated with PSNR, SSIM, LPIPS, and FVD, including comparisons on the TikTok benchmark and a proposed generalized-scenario dataset.The TikTok evaluation uses static backgrounds, while the proposed dataset targets diverse scenes, actions, and human-object interactions.
- Comparisons: On the proposed dataset, Animate Anyone 2 significantly outperforms alternative approaches in character animation quality.The authors attribute this to motion modeling that generalizes across diverse patterns and decoupled environment and character generation.
- Comparisons: Compared with a pseudo character-environment integration baseline, the method provides enhanced character-environment integration and avoids preprocessing artifacts associated with background inpainting.The comparison also reports erroneous character-background relationships for MIMO when preprocessing artifacts remain.
- Ablation Study: Accurate character masks can deform appearance, whereas bounding boxes can distort nearby scene context; the proposed environment formulation avoids these issues.The ablation compares precise masks and bounding-box regions with the proposed formulation.
- Ablation Study: Without pose modulation, limb relationships show misalignment and spatial inconsistency; depth-aware modulation improves learning of complex limb relationships across challenging motions.The pose-modulation ablation is reported qualitatively and quantitatively.
5. Discussion and Conclusion
Animate Anyone 2 enables character animation with environment affordance through environmental information, environment formulation, object injection, and pose modulation. The paper reports high-fidelity generation while acknowledging limitations in small-region hand-object interactions, shape discrepancies, and segmentation quality.
- Limitations: Complex hand-object interactions may produce artifacts when the interaction occupies a small pixel region or source and target characters differ substantially in shape.Object-interaction performance is also influenced by SAM’s segmentation capabilities.
- Conclusion: The method extracts environmental information from driving videos so animated characters preserve their original environments.It uses environment formulation and object injection for seamless character-environment integration.
- Conclusion: Animate Anyone 2 uses pose modulation to handle diverse motion patterns and reports high-fidelity generation performance.The conclusion presents pose modulation as part of the framework’s contribution.