title: Animate Anyone 2: High-Fidelity Character Image Animation with
    Environment Affordance
summary: 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.
paper_authors._id: 67ad9fb9731ff0d7da9f40e9
paper_authors.name: Li Hu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:21:24.286Z
paper_authors._id: 67ad9f06040354c9105b00bc
paper_authors.avatarUrl: /avatars/39e9f4c48c93bb33f155390653936fc1.svg
paper_authors.fullname: LiHu
paper_authors.type: user
paper_authors.user: Hookszdp
paper_authors._id: 67ad9fb9731ff0d7da9f40ea
paper_authors.name: Guangyuan Wang
paper_authors._id: 67ad9fb9731ff0d7da9f40eb
paper_authors.name: Zhen Shen
paper_authors._id: 67ad9fb9731ff0d7da9f40ec
paper_authors.name: Xin Gao
paper_authors._id: 67ad9fb9731ff0d7da9f40ed
paper_authors.name: Dechao Meng
paper_authors._id: 67ad9fb9731ff0d7da9f40ee
paper_authors.name: Lian Zhuo
paper_authors._id: 67ad9fb9731ff0d7da9f40ef
paper_authors.name: Peng Zhang
paper_authors._id: 67ad9fb9731ff0d7da9f40f0
paper_authors.name: Bang Zhang
paper_authors._id: 67ad9fb9731ff0d7da9f40f1
paper_authors.name: Liefeng Bo
paper_title: Animate Anyone 2: High-Fidelity Character Image Animation with
    Environment Affordance
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: diffusion models Animate Anyone environment affordance conditional inputs shape-agnostic mask object guider spatial blending pose modulation