FlowDreamer: Exploring High Fidelity Text-to-3D Generation via Rectified Flow

• URL: http://arxiv.org/abs/2408.05008v3
• Date: Wed, 9 Oct 2024 06:05:53 GMT
• Title: FlowDreamer: Exploring High Fidelity Text-to-3D Generation via Rectified Flow
• Authors: Hangyu Li, Xiangxiang Chu, Dingyuan Shi, Wang Lin,
• Abstract summary: We propose a novel framework, named FlowDreamer, which yields high fidelity results with richer textual details and faster convergence. Key insight is to leverage the coupling and reversible properties of the rectified flow model to search for the corresponding noise. We introduce a novel Unique Matching Couple (UCM) loss, which guides the 3D model to optimize along the same trajectory.
• Score: 17.919092916953183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in text-to-3D generation have made significant progress. In particular, with the pretrained diffusion models, existing methods predominantly use Score Distillation Sampling (SDS) to train 3D models such as Neural RaRecent advances in text-to-3D generation have made significant progress. In particular, with the pretrained diffusion models, existing methods predominantly use Score Distillation Sampling (SDS) to train 3D models such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D GS). However, a hurdle is that they often encounter difficulties with over-smoothing textures and over-saturating colors. The rectified flow model -- which utilizes a simple ordinary differential equation (ODE) to represent a straight trajectory -- shows promise as an alternative prior to text-to-3D generation. It learns a time-independent vector field, thereby reducing the ambiguity in 3D model update gradients that are calculated using time-dependent scores in the SDS framework. In light of this, we first develop a mathematical analysis to seamlessly integrate SDS with rectified flow model, paving the way for our initial framework known as Vector Field Distillation Sampling (VFDS). However, empirical findings indicate that VFDS still results in over-smoothing outcomes. Therefore, we analyze the grounding reasons for such a failure from the perspective of ODE trajectories. On top, we propose a novel framework, named FlowDreamer, which yields high fidelity results with richer textual details and faster convergence. The key insight is to leverage the coupling and reversible properties of the rectified flow model to search for the corresponding noise, rather than using randomly sampled noise as in VFDS. Accordingly, we introduce a novel Unique Couple Matching (UCM) loss, which guides the 3D model to optimize along the same trajectory.

Related papers

• VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation [69.68568248073747]
  We propose Pose-dependent Consistency Distillation Sampling (PCDS), a novel yet efficient objective for diffusion-based 3D generation tasks. PCDS builds the pose-dependent consistency function within diffusion trajectories, allowing to approximate true gradients through minimal sampling steps. For efficient generation, we propose a coarse-to-fine optimization strategy, which first utilizes 1-step PCDS to create the basic structure of 3D objects, and then gradually increases PCDS steps to generate fine-grained details.
  arXiv  Detail & Related papers  (2024-06-21T08:21:52Z)
• ExactDreamer: High-Fidelity Text-to-3D Content Creation via Exact Score Matching [10.362259643427526]
  Current approaches often adapt pre-trained 2D diffusion models for 3D synthesis. Over-smoothing poses a significant limitation on the high-fidelity generation of 3D models. LucidDreamer replaces the Denoising Diffusion Probabilistic Model (DDPM) in SDS with the Denoising Diffusion Implicit Model (DDIM)
  arXiv  Detail & Related papers  (2024-05-24T20:19:45Z)
• SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior [53.52396082006044]
  Current methods struggle to maintain rendering quality at the viewpoint that deviates significantly from the training viewpoints. This issue stems from the sparse training views captured by a fixed camera on a moving vehicle. We propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model.
  arXiv  Detail & Related papers  (2024-03-29T09:20:29Z)
• Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior [87.55592645191122]
  Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation, but are vulnerable to geometry collapse and poor textures yet. We propose a novel and effective "Consistent3D" method that explores the ODE deterministic sampling prior for text-to-3D generation. Experimental results show the efficacy of our Consistent3D in generating high-fidelity and diverse 3D objects and large-scale scenes.
  arXiv  Detail & Related papers  (2024-01-17T08:32:07Z)
• CAD: Photorealistic 3D Generation via Adversarial Distillation [28.07049413820128]
  We propose a novel learning paradigm for 3D synthesis that utilizes pre-trained diffusion models. Our method unlocks the generation of high-fidelity and photorealistic 3D content conditioned on a single image and prompt.
  arXiv  Detail & Related papers  (2023-12-11T18:59:58Z)
• Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting [60.393072253444934]
  We propose a unified framework aimed at enhancing the diffusion priors for 3D generation tasks. We identify a divergence between the diffusion priors and the training procedures of diffusion models that substantially impairs the quality of 3D generation.
  arXiv  Detail & Related papers  (2023-12-08T03:55:34Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching [33.696757740830506]
  Recent advancements in text-to-3D generation have shown promise. Many methods base themselves on Score Distillation Sampling (SDS) We propose Interval Score Matching (ISM) to counteract over-smoothing.
  arXiv  Detail & Related papers  (2023-11-19T09:59:09Z)
• Learned Vertex Descent: A New Direction for 3D Human Model Fitting [64.04726230507258]
  We propose a novel optimization-based paradigm for 3D human model fitting on images and scans. Our approach is able to capture the underlying body of clothed people with very different body shapes, achieving a significant improvement compared to state-of-the-art. LVD is also applicable to 3D model fitting of humans and hands, for which we show a significant improvement to the SOTA with a much simpler and faster method.
  arXiv  Detail & Related papers  (2022-05-12T17:55:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.