Animate Any Character in Any World
Recent advances in world models have greatly enhanced interactive environment simulation. Existing methods mainly fall into two categories: (1) static world generation models, which construct 3D environments without active agents, and (2) controllable-entity models, which allow a single entity to perform limited actions in an otherwise uncontrollable environment. In this work, we introduce AniX, leveraging the realism and structural grounding of static world generation while extending controllable-entity models to support user-specified characters capable of performing open-ended actions. Users can provide a 3DGS scene and a character, then direct the character through natural language to perform diverse behaviors from basic locomotion to object-centric interactions while freely exploring the environment. AniX synthesizes temporally coherent video clips that preserve visual fidelity with the provided scene and character, formulated as a conditional autoregressive video generation problem. Built upon a pre-trained video generator, our training strategy significantly enhances motion dynamics while maintaining generalization across actions and characters. Our evaluation covers a broad range of aspects, including visual quality, character consistency, action controllability, and long-horizon coherence.
6948b1b934f46eaf46cbb284
Yitong Wang
claimed_verified
2025-12-25T20:51:59.238Z
645db15ff4f49de580a10269
/avatars/ea1bdd7a478f4c4a7b3e134c4330ec78.svg
snowflakewang
SnowflakeWang
user
SnowflakeWang
6948b1b934f46eaf46cbb285
Fangyun Wei
6948b1b934f46eaf46cbb286
Hongyang Zhang
6948b1b934f46eaf46cbb287
Bo Dai
6948b1b934f46eaf46cbb288
Yan Lu
Animate Any Character in Any World
Recent advances in world models have greatly enhanced interactive environment simulation. Existing methods mainly fall into two categories: (1) static world generation models, which construct 3D environments without active agents, and (2) controllable-entity models, which allow a single entity to perform limited actions in an otherwise uncontrollable environment. In this work, we introduce AniX, leveraging the realism and structural grounding of static world generation while extending controllable-entity models to support user-specified characters capable of performing open-ended actions. Users can provide a 3DGS scene and a character, then direct the character through natural language to perform diverse behaviors from basic locomotion to object-centric interactions while freely exploring the environment. AniX synthesizes temporally coherent video clips that preserve visual fidelity with the provided scene and character, formulated as a conditional autoregressive video generation problem. Built upon a pre-trained video generator, our training strategy significantly enhances motion dynamics while maintaining generalization across actions and characters. Our evaluation covers a broad range of aspects, including visual quality, character consistency, action controllability, and long-horizon coherence.
AniX synthesizes temporally coherent videos by extending controllable-entity models to support diverse, user-defined character interactions in static 3D environments using conditional autoregressive video generation.
world models 3D environments static world generation controllable-entity models AniX 3DGS scene natural language object-centric interactions conditional autoregressive video generation video generator motion dynamics generalization visual quality character consistency action controllability long-horizon coherence