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New CogPortrait Framework Enhances Portrait Animation with Fine-Grained Eye Control

Researchers have introduced CogPortrait, a novel two-stage framework designed for generating portrait animations with fine-grained control over the eye region. This system utilizes three chain-of-thought Multimodal Large Language Models (MLLMs) agents to translate high-level labels into detailed facial keypoints. A DiT-based video generation backbone then synthesizes the animation, incorporating advanced techniques for enhanced visual quality and identity consistency, particularly in challenging boundary cases. AI

IMPACT This research introduces a novel approach to portrait animation, potentially improving the realism and expressiveness of AI-generated characters by offering more precise control over facial features like the eyes.

RANK_REASON The cluster contains a research paper detailing a new framework for portrait animation.

Read on arXiv cs.CV →

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · He Feng, Yongjia Ma, Donglin Di, Lei Fan, Tonghua Su ·

    CogPortrait: Fine-Grained Eye-Region Control in Portrait Animation via Hierarchical Agent Planning

    arXiv:2605.28056v1 Announce Type: new Abstract: Portrait animation methods have achieved substantial visual quality and lip synchronization, but fine-grained manipulation of the eye region still faces a trade-off between input granularity and motion accuracy. Existing methods usi…

  2. arXiv cs.CV TIER_1 English(EN) · Tonghua Su ·

    CogPortrait: Fine-Grained Eye-Region Control in Portrait Animation via Hierarchical Agent Planning

    Portrait animation methods have achieved substantial visual quality and lip synchronization, but fine-grained manipulation of the eye region still faces a trade-off between input granularity and motion accuracy. Existing methods using emotion labels or coarse text prompts are ins…