Vision-Language Models as Differentiable Semantic and Spatial Rewards for Text-to-3D Generation

• URL: http://arxiv.org/abs/2509.15772v1
• Date: Fri, 19 Sep 2025 08:54:52 GMT
• Title: Vision-Language Models as Differentiable Semantic and Spatial Rewards for Text-to-3D Generation
• Authors: Weimin Bai, Yubo Li, Weijian Luo, Wenzheng Chen, He Sun,
• Abstract summary: We propose VLM3D, a novel text-to-3D generation framework.<n>It integrates large vision-language models into the Score Distillation Sampling pipeline as differentiable semantic and spatial priors.<n>VLM3D significantly outperforms prior SDS-based methods in semantic fidelity, geometric coherence, and spatial correctness.
• Score: 23.359745449828363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Score Distillation Sampling (SDS) enables high-quality text-to-3D generation by supervising 3D models through the denoising of multi-view 2D renderings, using a pretrained text-to-image diffusion model to align with the input prompt and ensure 3D consistency. However, existing SDS-based methods face two fundamental limitations: (1) their reliance on CLIP-style text encoders leads to coarse semantic alignment and struggles with fine-grained prompts; and (2) 2D diffusion priors lack explicit 3D spatial constraints, resulting in geometric inconsistencies and inaccurate object relationships in multi-object scenes. To address these challenges, we propose VLM3D, a novel text-to-3D generation framework that integrates large vision-language models (VLMs) into the SDS pipeline as differentiable semantic and spatial priors. Unlike standard text-to-image diffusion priors, VLMs leverage rich language-grounded supervision that enables fine-grained prompt alignment. Moreover, their inherent vision language modeling provides strong spatial understanding, which significantly enhances 3D consistency for single-object generation and improves relational reasoning in multi-object scenes. We instantiate VLM3D based on the open-source Qwen2.5-VL model and evaluate it on the GPTeval3D benchmark. Experiments across diverse objects and complex scenes show that VLM3D significantly outperforms prior SDS-based methods in semantic fidelity, geometric coherence, and spatial correctness.

Related papers

• Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation [34.44214123004662]
  We propose VLM3D, a framework for differentiable semantic and spatial critics.<n>Our core contribution is a dual-language critic signal derived from the VLM's Yes or No log-odds.<n>VLM3D establishes a principled and general path to inject the VLM's rich, language-grounded understanding of both semantics and space into diverse 3D generative pipelines.
  arXiv  Detail & Related papers  (2025-11-18T09:05:26Z)
• Abstract 3D Perception for Spatial Intelligence in Vision-Language Models [100.13033631690114]
  Vision-language models (VLMs) struggle with 3D-related tasks such as spatial cognition and physical understanding.<n>We introduce SandboxVLM, a framework that leverages abstract bounding boxes to encode geometric structure and physical kinematics for VLM.<n>Our approach consistently improves spatial intelligence, achieving an 8.3% gain on SAT Real compared with baseline methods.
  arXiv  Detail & Related papers  (2025-11-14T04:16:09Z)
• IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction [82.53307702809606]
  Humans naturally perceive the geometric structure and semantic content of a 3D world as intertwined dimensions.<n>We propose InstanceGrounded Geometry Transformer (IGGT) to unify the knowledge for both spatial reconstruction and instance-level contextual understanding.
  arXiv  Detail & Related papers  (2025-10-26T14:57:44Z)
• Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding [11.222744122842023]
  We introduce a plug-and-play module that implicitly incorporates 3D geometry features into Vision-Language-Action (VLA) models.<n>Our method significantly improves the performance of state-of-the-art VLA models across diverse scenarios.
  arXiv  Detail & Related papers  (2025-07-01T04:05:47Z)
• 3D-Aware Vision-Language Models Fine-Tuning with Geometric Distillation [17.294440057314812]
  Vision-Language Models (VLMs) have shown remarkable performance on diverse visual and linguistic tasks.<n>We propose Geometric Distillation, a framework that injects human-inspired geometric cues into pretrained VLMs.<n>Our method shapes representations to be geometry-aware while remaining compatible with natural image-text inputs.
  arXiv  Detail & Related papers  (2025-06-11T15:56:59Z)
• VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction [86.82819259860186]
  We introduce VLM-3R, a unified framework for Vision-Language Models (VLMs) that incorporates 3D Reconstructive instruction tuning.<n>VLM-3R processes monocular video frames by employing a geometry encoder to derive implicit 3D tokens that represent spatial understanding.
  arXiv  Detail & Related papers  (2025-05-26T17:56:30Z)
• CoherenDream: Boosting Holistic Text Coherence in 3D Generation via Multimodal Large Language Models Feedback [18.857087708269038]
  Score Distillation Sampling (SDS) has achieved remarkable success in text-to-3D content generation.<n>SDS-based methods struggle to maintain semantic fidelity for user prompts.<n>We propose Textual Coherent Score Distillation (TCSD), which integrates alignment feedback from multimodal large language models (MLLMs)
  arXiv  Detail & Related papers  (2025-04-28T14:50:45Z)
• MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation [87.30919771444117]
  Reasoning segmentation aims to segment target objects in complex scenes based on human intent and spatial reasoning.<n>Recent multimodal large language models (MLLMs) have demonstrated impressive 2D image reasoning segmentation.<n>We introduce MLLM-For3D, a framework that transfers knowledge from 2D MLLMs to 3D scene understanding.
  arXiv  Detail & Related papers  (2025-03-23T16:40:20Z)
• Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene Understanding [58.38294408121273]
  We propose Cross-modal and Uncertainty-aware Agglomeration for Open-vocabulary 3D Scene Understanding dubbed CUA-O3D.<n>Our method addresses two key challenges: (1) incorporating semantic priors from VLMs alongside the geometric knowledge of spatially-aware vision foundation models, and (2) using a novel deterministic uncertainty estimation to capture model-specific uncertainties.
  arXiv  Detail & Related papers  (2025-03-20T20:58:48Z)
• SeMv-3D: Towards Concurrency of Semantic and Multi-view Consistency in General Text-to-3D Generation [122.47961178994456]
  SeMv-3D is a novel framework that jointly enhances semantic alignment and multi-view consistency in GT23D generation.<n>At its core, we introduce Triplane Prior Learning (TPL), which effectively learns triplane priors.<n>We also present Prior-based Semantic Aligning in Triplanes (SAT), which enables consistent any-view synthesis.
  arXiv  Detail & Related papers  (2024-10-10T07:02:06Z)

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.