Active Intelligence in Video Avatars via Closed-loop World Modeling
Current video avatar generation methods excel at identity preservation and motion alignment but lack genuine agency, they cannot autonomously pursue long-term goals through adaptive environmental interaction. We address this by introducing L-IVA (Long-horizon Interactive Visual Avatar), a task and benchmark for evaluating goal-directed planning in stochastic generative environments, and ORCA (Online Reasoning and Cognitive Architecture), the first framework enabling active intelligence in video avatars. ORCA embodies Internal World Model (IWM) capabilities through two key innovations: (1) a closed-loop OTAR cycle (Observe-Think-Act-Reflect) that maintains robust state tracking under generative uncertainty by continuously verifying predicted outcomes against actual generations, and (2) a hierarchical dual-system architecture where System 2 performs strategic reasoning with state prediction while System 1 translates abstract plans into precise, model-specific action captions. By formulating avatar control as a POMDP and implementing continuous belief updating with outcome verification, ORCA enables autonomous multi-step task completion in open-domain scenarios. Extensive experiments demonstrate that ORCA significantly outperforms open-loop and non-reflective baselines in task success rate and behavioral coherence, validating our IWM-inspired design for advancing video avatar intelligence from passive animation to active, goal-oriented behavior.
694b6889746a34b55dd53dd2
Xuanhua He
694b6889746a34b55dd53dd3
Tianyu Yang
694b6889746a34b55dd53dd4
Ke Cao
694b6889746a34b55dd53dd5
Ruiqi Wu
claimed_verified
2025-12-29T14:24:48.280Z
6530c9a5f4c60acf0cc9259a
https://cdn-avatars.huggingface.co/v1/production/uploads/6530c9a5f4c60acf0cc9259a/B_8QPI39LYVcVN8mVdvtz.png
Ruiqi Wu
wuruiqi0722
user
wuruiqi0722
694b6889746a34b55dd53dd6
Cheng Meng
694b6889746a34b55dd53dd7
Yong Zhang
694b6889746a34b55dd53dd8
Zhuoliang Kang
694b6889746a34b55dd53dd9
Xiaoming Wei
694b6889746a34b55dd53dda
Qifeng Chen
Active Intelligence in Video Avatars via Closed-loop World Modeling
Current video avatar generation methods excel at identity preservation and motion alignment but lack genuine agency, they cannot autonomously pursue long-term goals through adaptive environmental interaction. We address this by introducing L-IVA (Long-horizon Interactive Visual Avatar), a task and benchmark for evaluating goal-directed planning in stochastic generative environments, and ORCA (Online Reasoning and Cognitive Architecture), the first framework enabling active intelligence in video avatars. ORCA embodies Internal World Model (IWM) capabilities through two key innovations: (1) a closed-loop OTAR cycle (Observe-Think-Act-Reflect) that maintains robust state tracking under generative uncertainty by continuously verifying predicted outcomes against actual generations, and (2) a hierarchical dual-system architecture where System 2 performs strategic reasoning with state prediction while System 1 translates abstract plans into precise, model-specific action captions. By formulating avatar control as a POMDP and implementing continuous belief updating with outcome verification, ORCA enables autonomous multi-step task completion in open-domain scenarios. Extensive experiments demonstrate that ORCA significantly outperforms open-loop and non-reflective baselines in task success rate and behavioral coherence, validating our IWM-inspired design for advancing video avatar intelligence from passive animation to active, goal-oriented behavior.
ORCA, a framework for goal-directed planning in video avatars, uses an internal world model and dual-system architecture to enable autonomous task completion in stochastic environments.
L-IVA ORCA Internal World Model IWM closed-loop OTAR cycle Observe-Think-Act-Reflect hierarchical dual-system architecture POMDP belief updating outcome verification