Kavli Affiliate: Yi Zhou
| First 5 Authors: Jiahao Zhu, Jiahao Zhu, , ,
| Summary:
Recent advancements in text-to-3D generation improve the visual quality of
Score Distillation Sampling (SDS) and its variants by directly connecting
Consistency Distillation (CD) to score distillation. However, due to the
imbalance between self-consistency and cross-consistency, these CD-based
methods inherently suffer from improper conditional guidance, leading to
sub-optimal generation results. To address this issue, we present
SegmentDreamer, a novel framework designed to fully unleash the potential of
consistency models for high-fidelity text-to-3D generation. Specifically, we
reformulate SDS through the proposed Segmented Consistency Trajectory
Distillation (SCTD), effectively mitigating the imbalance issues by explicitly
defining the relationship between self- and cross-consistency. Moreover, SCTD
partitions the Probability Flow Ordinary Differential Equation (PF-ODE)
trajectory into multiple sub-trajectories and ensures consistency within each
segment, which can theoretically provide a significantly tighter upper bound on
distillation error. Additionally, we propose a distillation pipeline for a more
swift and stable generation. Extensive experiments demonstrate that our
SegmentDreamer outperforms state-of-the-art methods in visual quality, enabling
high-fidelity 3D asset creation through 3D Gaussian Splatting (3DGS).
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