Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance (2502.06145v1)

Abstract: 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.

• The paper presents a novel framework that integrates environmental affordance with character image animation for realistic, context-driven results.
• It leverages an object guider mechanism and depth-wise pose modulation to enhance interactions and accurately capture complex character poses.
• The approach outperforms traditional diffusion-based models on benchmarks, achieving superior consistency and detailed animation quality.

Overview of "Animate Anyone 2"

The paper "Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance" introduces an innovative framework for character image animation that not only synthesizes animations from motion signals but also incorporates environmental context, a feature that previous methods have largely overlooked. The authors propose a method that leverages both character and environmental representations from a driving video, allowing for high-fidelity character animations that maintain consistency with their surroundings – a feature termed "environment affordance."

Character image animation has been extensively explored with diffusion-based models, which are praised for their consistency, stability, and generalizability in animating characters. These models, however, lack the ability to reasonably integrate characters within their interactive environments, often resulting in superficial animations that ignore complex spatial relationships and object interactions. "Animate Anyone 2" addresses this gap by formulating the environment as the character-excluded region and developing a novel, shape-agnostic mask strategy to better represent the boundary relationship between characters and their surroundings.

Methodological Advances

Three significant methodological innovations are central to this framework:

1. Environment and Object Integration: The authors introduce an object guider mechanism, which enhances the fidelity of object-character interactions. This involves extracting features from interactive objects within the video and integrating them into the animation process through spatial blending techniques. By doing so, "Animate Anyone 2" can preserve intricate object interactions and dynamic contexts that are often missing in conventional animation paradigms.
2. Pose Modulation: A depth-wise pose modulation strategy is proposed, which improves the model's capability to handle diverse and complex character poses. This strategy incorporates depth information into the motion modeling process, effectively enhancing the robustness of animations in diverse scenarios by more accurately representing spatial limb relationships.
3. Shape-Agnostic Mask Strategy: The researchers propose a mask strategy that eschews the pitfalls of strict boundary formulations, allowing for more flexible character generation while maintaining environmental coherence. This strategy randomly divides character mask regions to break boundary correspondence, compelling the model to learn better context integration without shape leakage.

Results and Implications

The proposed framework demonstrates superior performance on various benchmarks when compared to existing methods. On the TikTok Benchmark, where backgrounds are static, their method outperforms others by achieving high scores in SSIM, PSNR, LPIPS, and FVD, reflecting its superior capability to generate consistent and high-quality animations. Furthermore, on the authors' custom dataset, which incorporates a wider and more challenging variety of scenes, "Animate Anyone 2" excels by maintaining character consistency and seamlessly integrating characters into their environments.

Practically, this innovation holds significant potential for filmmaking, advertising, and virtual applications requiring realistic and dynamic character-environment interactions. Theoretically, this research opens pathways for further exploration into integrating multi-modal signals beyond movement and context, potentially incorporating audio cues or interactive feedback from the environment. As AI continues to evolve, the continual refinement of models like "Animate Anyone 2" suggests a future where digital characters can inhabit virtual spaces with unprecedented realism.

In summary, "Animate Anyone 2" demonstrates a sophisticated approach to character animation, emphasizing integration with environmental affordances, which serves as a substantial leap forward in the field of generative video animations. As the landscape of AI in animation progresses, this paper suggests promising directions for further research into richer, context-aware, and interactive digital world-building.