Generating Material-Aware 3D Models from Sparse Views
Bibliographic record
Abstract
Image-to-3D diffusion models have significantly advanced 3D content generation. However, existing methods often struggle to disentangle material and illumination from coupled appearance, as they primarily focus on modeling geometry and appearance. This paper introduces a novel approach to generate material-aware 3D models from sparse-view images using generative models and efficient pre-integrated rendering. The output of our method is a relightable model that independently models geometry, material, and lighting, enabling downstream tasks to manipulate these components separately. To fully leverage information from limited sparse views, we propose a mixed supervision framework that simultaneously exploits view-consistency via captured views and diffusion prior via generating views. Additionally, a view selection mechanism is proposed to mitigate the degenerated diffusion prior. We adapt an efficient yet powerful pre-integrated rendering pipeline to factorize the scene into a differentiable environment illumination, a spatially varying material field, and an implicit SDF field. Our experiments on both real-world and synthetic datasets demonstrate the effectiveness of our approach in decomposing each component as well as manipulating the illumination. Source codes are available at https://github.com/Sheldonmao/MatSparse3D.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".