MoCoTalk: Multi-Conditional Diffusion with Adaptive Router for Controllable Talking Head Generation

• URL: http://arxiv.org/abs/2605.08050v1
• Date: Fri, 08 May 2026 17:40:01 GMT
• Title: MoCoTalk: Multi-Conditional Diffusion with Adaptive Router for Controllable Talking Head Generation
• Abstract summary: MoCoTalk is a multi-conditional video diffusion framework that unifies four complementary control signals.<n> Adaptive Multi-Condition Router computes channel-wise, timestep-aware gating over the four condition streams.<n> Mouth-Augmented Shading Mesh is a 3DMM-based representation that decouples head motion, mouth motion, expression, and lighting.
• Score: 45.88028371034407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Talking-head generation requires joint modeling of identity, head pose, facial expression, and mouth dynamics. Existing methods typically address only a subset of these factors, and rely on fixed-weight or heuristic fusion when multiple conditions are involved. We present MoCoTalk, a multi-conditional video diffusion framework that unifies four complementary control signals: a reference image, facial keypoints, 3DMM-rendered shading meshes, and the corresponding speech audio. To resolve destructive interference among heterogeneous conditions, we introduce an Adaptive Multi-Condition Router that computes channel-wise, timestep-aware gating over the four condition streams, allowing the fusion strategy to vary with both feature subspace and noise level. To better capture speech-related facial dynamics, we design a Mouth-Augmented Shading Mesh, a 3DMM-based representation that decouples head motion, mouth motion, expression, and lighting. This design provides a temporally consistent geometric prior and allows flexible recombination of these attributes at inference. We further introduce a lip consistency loss to tighten audio-visual alignment. Extensive experiments show that MoCoTalk achieves state-of-the-art performance on the majority of structural, motion, and perceptual metrics, while offering attribute-level controllability that single-condition methods do not provide.

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