Fangfu Liu | 刘芳甫

I'm a second-year PhD student in the Department of Electronic Engineering at Tsinghua University , advised by Prof. Yueqi Duan. In 2023, I obtained my B.Eng. in the Department of Electronic Engineering, Tsinghua University.

My research interest lies in Machine Learning and 3D Computer Vision (e.g., 3D AIGC). I aim to build spatially intelligent AI that can model the world and reason about objects, places, and interactions in 3D space and time.

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Preprints

*Equal contribution    ^†Project leader

In this paper, we introduce DimensionX, a framework designed to generate photorealistic 3D and 4D scenes from just a single image with video diffusion. We believe that our research provides a promising direction to create a dynamic and interactive environment with video diffusion models.

In this paper, we propose ReconX, a novel 3D scene reconstruction paradigm that reframes the ambiguous reconstruction challenge as a temporal generation task. The key insight is to unleash the strong generative prior of large pre-trained video diffusion models for sparse-view reconstruction.

In this paper, we propose DreamCinema, a novel cinematic transfer framework that pioneers generative AI into the film production paradigm, aiming at facilitating user-friendly film creation.

In this paper, we propose Physics3D, a novel method for learning various physical properties of 3D objects through a video diffusion model. Our approach involves designing a highly generalizable physical simulation system based on a viscoelastic material model, which enables us to simulate a wide range of materials with high-fidelity capabilities.

Selected Publications

*Equal contribution    ^†Project leader

In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation fidelity and strong generalizability. Unique3D can generate a high-fidelity textured mesh from a single orthogonal RGB image of any object in under 30 seconds.

In this work, We propose the first general-purpose human preference reward model for text-to-3D generation, named Reward3D. Then we further introduce a novel text-to-3D framework, coined DreamReward, which greatly boosts high-text alignment and high-quality 3D generation through human preference feedback.

We introduce a novel 3D customization method, dubbed Make-Your-3D that can personalize high-fidelity and consistent 3D content from only a single image of a subject with text description within 5 minutes.

We propose Sherpa3D, a new text-to-3D framework that achieves high-fidelity, generalizability, and geometric consistency simultaneously. Extensive experiments show the superiority of our Sherpa3D over the state-of-the-art text-to-3D methods in terms of quality and 3D consistency.

we propose a dynamic causal space for DAG structure learning, coined CASPER, that integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and groundtruth DAG.

We propose a neural semantic representation called Semantic-Ray (S-Ray) to build a generalizable semantic field, which is able to learn from multiple scenes and directly infer semantics on novel viewpoints across novel scenes.

We propose ReScore, a simple-yet-effective model-agnostic optimzation framework that simultaneously eliminates spurious edge learning and generalizes to heterogeneous data by utilizing learnable adaptive weights.

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