Animate Anyone 2: High-Fidelity Character Image Animation with
  Environment Affordance
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.
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Li Hu
claimed_verified
2025-02-13T08:21:24.286Z
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LiHu
user
Hookszdp
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Guangyuan Wang
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Zhen Shen
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Xin Gao
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Dechao Meng
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Lian Zhuo
67ad9fb9731ff0d7da9f40ef
Peng Zhang
67ad9fb9731ff0d7da9f40f0
Bang Zhang
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Liefeng Bo
Animate Anyone 2: High-Fidelity Character Image Animation with
  Environment Affordance
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 proposed Animate Anyone 2 model generates character animations that consider environmental context and object interactions, using a shape-agnostic mask and pose modulation strategy.
diffusion models Animate Anyone environment affordance conditional inputs shape-agnostic mask object guider spatial blending pose modulation