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Gaofeng Liu

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2 papers

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AAAI Conference 2025 Conference Paper

DreamAlign: Dynamic Text-to-3D Optimization with Human Preference Alignment

Gaofeng Liu
Zhiyuan Ma
Tao Fang

Recent years have witnessed the remarkable success of Text-to-3D generation, particularly with the rise of mainstream conditional diffusion models (DMs). Though achieving substantial progress, existing methods still face a knotty "human preference" dilemma, that is the 3D contents generated by the models often deviate greatly from the desired effects (e.g., perspective, aesthetics, shading, appearance, etc.) due to the lack of attention to human preferences. To mitigate the limitation of data deficiency and enable human preference learning, we first elaborately curate the HP3D, a text-to-3D dataset with expert preference annotations which is initally captioned by the multimodal large model LLava and then refined by human expert. Based on such a brand-new HP3D, we further propose DreamAlign, a reward-free method that does not require designing any complex reward models whereas only by introducing a light-weight lora adapter and then designing a novel direct 3D preference optimization (D-3DPO) algorithm for training. Moreover, in the stage of text-to-3D we design an additional Preference Contrastive Feedback training for score distillation sampling, which enables the generated 3D objects to align the human preferences (e.g., aesthetics, material, etc.). Extensive experiments demonstrate that DreamAlign consistently achieves state-of-the-art performance on generative effects and human preference alignment across various benchmark evaluations.

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IROS Conference 2025 Conference Paper

EmoRLTalk: Speech-Driven Emotional Facial Animation With Offline Reinforcement Learning

Gaofeng Liu
Xuetong Li
Ruoyu Gao
Ye Yuan
Jian Liu
Hengsen Li
Hong Huo
Tao Fang

In recent years, significant breakthroughs have been made in audio-guided 3D facial animation. However, existing methods mainly focus on lip shape and audio consistency and still face key challenges to achieve alignment between facial emotions and speech emotions. To overcome this limitation, we introduce EmoRLTalk, a novel framework that integrates offline reinforcement learning to implicitly capture the intricate relationship between 3D facial landmarks and blendshape parameters, thereby enhancing the granularity of emotional expression. Furthermore, we harness the strength of conditional diffusion models to synthesize facial motions that are emotionally coherent with the input speech. Additionally, based on the multi-task learning paradigm, we construct a collaborative training framework of a regression main task and a classification sub-task. Specifically, we use emotion classification of blendshape as a sub-task to further improve the model’s ability to express facial emotions. To further enhance system controllability, we integrate the ControlNet module, allowing users to achieve precise facial expression control. Extensive experiments demonstrate that EmoRLTalk achieves superior emotional expressiveness and lip-sync performance compared to previous approaches.

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