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
2025-02-13
Summary
This paper talks about Animate Anyone 2, a new AI system that creates more realistic and interactive character animations by considering the environment around the characters.
What's the problem?
Current AI methods for animating characters are good at making consistent and flexible animations, but they struggle to make the characters interact naturally with their surroundings. It's like having a really good puppet that can move smoothly, but doesn't know how to open doors or sit on chairs properly.
What's the solution?
The researchers created Animate Anyone 2, which does a few clever things. First, it looks at the environment in the video and treats it as important information. It uses a special mask technique that helps the AI understand how characters should fit into different spaces. The system also has a way to make characters interact better with objects, like making sure a hand looks right when opening a door. Lastly, it can handle a wider range of movements, making the animations more diverse and natural.
Why it matters?
This matters because it brings us closer to creating really lifelike and interactive digital characters. Imagine watching a movie or playing a video game where the characters move and interact with their world just like real people would. This technology could make animations in films, games, and virtual reality feel much more realistic and immersive, potentially changing how we experience digital entertainment and even how we train AI for real-world tasks involving human-environment interaction.
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.