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Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 8.0Download Report PDF
Text-to-3D generationVideo Diffusion Model3D Gaussian SplattingGeneration
Abstract:

The rapid progress of large, pretrained models for both visual content generation and 3D reconstruction opens up new possibilities for text-to-3D generation. Intuitively, one could obtain a formidable 3D scene generator if one were able to combine the power of a modern latent text-to-video model as "generator" with the geometric abilities of a recent (feedforward) 3D reconstruction system as "decoder". We introduce VIST3A, a general framework that does just that, addressing two main challenges. First, the two components must be joined in a way that preserves the rich knowledge encoded in their weights. We revisit model stitching, i.e., we identify the layer in the 3D decoder that best matches the latent representation produced by the text-to-video generator and stitch the two parts together. That operation requires only a small dataset and no labels. Second, the text-to-video generator must be aligned with the stitched 3D decoder, to ensure that the generated latents are decodable into consistent, perceptually convincing 3D scene geometry. To that end, we adapt direct reward finetuning, a popular technique for human preference alignment. We evaluate the proposed VIST3A approach with different video generators and 3D reconstruction models. All tested pairings markedly improve over prior text-to-3D models that output Gaussian splats. Moreover, by choosing a suitable 3D base model, VIST3A also enables high-quality text-to-pointmap generation.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes VIST3A, a framework that stitches pretrained text-to-video generators with feedforward 3D reconstruction networks for text-to-3D generation. It resides in the 'Direct Video-to-3D Pipeline Integration' leaf, which contains only two papers including this one. This leaf sits within the broader 'Video-Guided Multi-View Generation and 3D Reconstruction' branch, indicating a relatively sparse but emerging research direction focused on seamless integration of video synthesis and 3D decoding without iterative refinement.

The taxonomy reveals neighboring approaches in sibling leaves: 'Iterative Multi-View Refinement' employs progressive diffusion cycles, 'Multi-View Diffusion with 3D Priors' trains models with explicit geometric awareness, and 'Video Temporal Consistency for 3D' exploits temporal coherence. VIST3A diverges by avoiding iterative loops or 3D-aware training, instead directly connecting pretrained components. The broader 'Scene-Level Generation' and 'Dynamic 4D Content Generation' branches address different scales and temporal dynamics, while VIST3A targets object-centric static generation through direct pipeline coupling.

Among 24 candidates examined, the model stitching contribution shows one refutable candidate from 5 examined, suggesting some prior work in connecting pretrained components. The VIST3A framework itself (9 candidates, 0 refutable) and direct reward finetuning for alignment (10 candidates, 0 refutable) appear more novel within this limited search scope. The statistics indicate that while the core integration strategy has minimal documented overlap, the underlying stitching technique has at least one closely related predecessor among the examined papers.

Based on top-24 semantic matches, VIST3A occupies a sparsely populated research direction with limited direct competition. The analysis does not cover exhaustive literature on model stitching in other domains or reward-based alignment techniques outside text-to-3D contexts. The framework's novelty appears strongest in its specific application of these techniques to video-3D integration, though the individual components draw on established methods.

Taxonomy

Core-task Taxonomy Papers
45
3
Claimed Contributions
22
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: text-to-3D generation by combining video models with 3D reconstruction networks. This emerging field leverages the temporal coherence and multi-view consistency inherent in video generation models to produce high-quality 3D assets from textual descriptions. The taxonomy reveals several major branches: Video-Guided Multi-View Generation and 3D Reconstruction focuses on direct pipelines that synthesize multi-view imagery or video sequences and then reconstruct geometry; Scene-Level Generation and Reconstruction extends these ideas to larger environments and compositional scenes; Dynamic 4D Content Generation tackles time-varying objects and motions; Specialized Generation Tasks addresses domain-specific challenges such as human avatars, texturing, or interactive editing; and Cross-Modal and Hybrid Approaches explore integration with other modalities or unconventional generation strategies. Representative works like MVDream[5] and Align Your Gaussians[4] illustrate how multi-view diffusion priors can be combined with 3D representations, while methods such as V3D[13] and VideoMV[18] demonstrate end-to-end video-to-3D pipelines. A particularly active line of work centers on direct video-to-3D pipeline integration, where researchers seek to minimize the gap between generated video frames and final 3D reconstructions. Stitching Multiview[0] exemplifies this direction by proposing techniques to stitch together multi-view video outputs into coherent 3D models, closely related to VIST3A[1], which similarly emphasizes seamless integration of video generation and reconstruction stages. These approaches contrast with methods like IM-3D[2] that rely on intermediate image-based representations, or dynamic generation frameworks such as Diffusion4D[3] that prioritize temporal evolution over static geometry. The original paper sits squarely within the direct pipeline integration cluster, sharing VIST3A[1]'s emphasis on tightly coupling video synthesis with reconstruction networks, yet differing in its specific stitching strategy to handle view consistency. This positioning highlights ongoing debates about whether to optimize video and 3D stages jointly or sequentially, and how best to preserve geometric fidelity across generated frames.

Claimed Contributions

VIST3A framework for text-to-3D generation via model stitching

The authors propose VIST3A, a framework that combines a text-to-video generator with a feedforward 3D reconstruction model through model stitching. This approach preserves the rich knowledge encoded in both pretrained components without requiring massive retraining or labeled data.

9 retrieved papers
Model stitching technique to construct 3D VAEs from pretrained components

The authors develop a model stitching method that identifies the optimal layer in a pretrained 3D reconstruction network to attach to a video VAE's latent space. This creates a 3D VAE by reusing rather than rebuilding 3D capabilities, requiring only a small unlabeled dataset.

4 retrieved papers
Direct reward finetuning for aligning video generators with 3D decoders

The authors adapt direct reward finetuning to align the text-to-video generator with the stitched 3D decoder. This ensures generated latents are 3D-consistent and decodable by maximizing rewards based on visual quality and 3D consistency throughout the denoising trajectory.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

VIST3A framework for text-to-3D generation via model stitching

The authors propose VIST3A, a framework that combines a text-to-video generator with a feedforward 3D reconstruction model through model stitching. This approach preserves the rich knowledge encoded in both pretrained components without requiring massive retraining or labeled data.

Contribution

Model stitching technique to construct 3D VAEs from pretrained components

The authors develop a model stitching method that identifies the optimal layer in a pretrained 3D reconstruction network to attach to a video VAE's latent space. This creates a 3D VAE by reusing rather than rebuilding 3D capabilities, requiring only a small unlabeled dataset.

Contribution

Direct reward finetuning for aligning video generators with 3D decoders

The authors adapt direct reward finetuning to align the text-to-video generator with the stitched 3D decoder. This ensures generated latents are 3D-consistent and decodable by maximizing rewards based on visual quality and 3D consistency throughout the denoising trajectory.

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