MoCha: Towards Movie-Grade Talking Character Synthesis
Recent advancements in video generation have achieved impressive motion
realism, yet they often overlook character-driven storytelling, a crucial task
for automated film, animation generation. We introduce Talking Characters, a
more realistic task to generate talking character animations directly from
speech and text. Unlike talking head, Talking Characters aims at generating the
full portrait of one or more characters beyond the facial region. In this
paper, we propose MoCha, the first of its kind to generate talking characters.
To ensure precise synchronization between video and speech, we propose a
speech-video window attention mechanism that effectively aligns speech and
video tokens. To address the scarcity of large-scale speech-labeled video
datasets, we introduce a joint training strategy that leverages both
speech-labeled and text-labeled video data, significantly improving
generalization across diverse character actions. We also design structured
prompt templates with character tags, enabling, for the first time,
multi-character conversation with turn-based dialogue-allowing AI-generated
characters to engage in context-aware conversations with cinematic coherence.
Extensive qualitative and quantitative evaluations, including human preference
studies and benchmark comparisons, demonstrate that MoCha sets a new standard
for AI-generated cinematic storytelling, achieving superior realism,
expressiveness, controllability and generalization.
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Bo Sun
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Haoyu Ma
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Ji Hou
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Felix Juefei-Xu
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Xu
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FelixXu
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Zecheng He
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Xiaoliang Dai
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Luxin Zhang
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Kunpeng Li
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Tingbo Hou
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Animesh Sinha
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Animesh Sinha
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Peter Vajda
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Wenhu Chen
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Wenhu Chen
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MoCha: Towards Movie-Grade Talking Character Synthesis
Recent advancements in video generation have achieved impressive motion
realism, yet they often overlook character-driven storytelling, a crucial task
for automated film, animation generation. We introduce Talking Characters, a
more realistic task to generate talking character animations directly from
speech and text. Unlike talking head, Talking Characters aims at generating the
full portrait of one or more characters beyond the facial region. In this
paper, we propose MoCha, the first of its kind to generate talking characters.
To ensure precise synchronization between video and speech, we propose a
speech-video window attention mechanism that effectively aligns speech and
video tokens. To address the scarcity of large-scale speech-labeled video
datasets, we introduce a joint training strategy that leverages both
speech-labeled and text-labeled video data, significantly improving
generalization across diverse character actions. We also design structured
prompt templates with character tags, enabling, for the first time,
multi-character conversation with turn-based dialogue-allowing AI-generated
characters to engage in context-aware conversations with cinematic coherence.
Extensive qualitative and quantitative evaluations, including human preference
studies and benchmark comparisons, demonstrate that MoCha sets a new standard
for AI-generated cinematic storytelling, achieving superior realism,
expressiveness, controllability and generalization.
MoCha generates realistic talking character animations from speech and text using a speech-video attention mechanism and joint training on speech-labeled and text-labeled data, enabling multi-character conversations and superior realism.
MoCha speech-video window attention mechanism joint training strategy structured prompt templates multi-character conversation cinematic coherence