以「動畫任何人2：具高保真度之角色影像動畫及環境可負擔性」為題。

Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance

February 10, 2025

作者: Li Hu, Guangyuan Wang, Zhen Shen, Xin Gao, Dechao Meng, Lian Zhuo, Peng Zhang, Bang Zhang, Liefeng Bo

cs.AI

摘要

基於擴散模型的最新角色形象動畫方法，例如Animate Anyone，已在生成一致且具有一般性的角色動畫方面取得顯著進展。然而，這些方法未能產生角色與其環境之間合理的關聯。為解決這一限制，我們引入了Animate Anyone 2，旨在為角色動畫增加環境適應性。除了從源視頻中提取運動信號外，我們還將環境表示形式捕捉為條件輸入。環境被定義為區域，不包括角色，我們的模型生成角色以填充這些區域，同時保持與環境背景的一致性。我們提出了一種形狀不可知的遮罩策略，更有效地描述角色與環境之間的關係。此外，為了增強物體交互作用的真實性，我們利用物體引導器提取交互物體的特徵，並採用空間混合進行特徵注入。我們還引入了一種姿勢調節策略，使模型能夠處理更多樣化的運動模式。實驗結果顯示了所提出方法的優越性能。

English

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.