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A3D: Does Diffusion Dream about 3D Alignment?
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Proposes A3D, which jointly generates a set of structurally aligned 3D objects by embedding them in a shared NeRF latent simplex, regularizing transitions between objects with SDS plausibility and smoothness constraints.
Problem
Text-to-3D generation produces collections of objects independently, so corresponding semantic parts lack structural alignment in pose and proportions across prompts.
Approach
Represent a set of objects and the transitions between them with one NeRF conditioned on a latent code on the probability simplex; apply SDS on simplex edges with blended text embeddings as a plausibility critic, and enforce smoothness via limited MLP depth and normal smoothness loss.
Key results
- On 15 prompt pairs, A3D achieves a DIFT distance of 6.1% of object size versus 27.7 CLIP for generation, outperforming MVEdit (5.5% DIFT, lower alignment-quality), LucidDreamer (11.3%), and GaussianEditor (2.4%) on DIFT only partially, while winning most GPTEval3D comparisons.
- Against MVDream independently generated objects, the full method reduces DIFT distance from 30.1% to 6.1% of object size.
- In structure-preserving transformation over 26 prompts, A3D reaches CLIP 27.8 and DIFT distance 7.9%, performing on par with MVEdit and GaussianEditor.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly optimize the aligned objects with A3D instead of generating them independently (ablation) | MVDream · Aligned 3D object generation | DIFT distance (% of object size): 30.1 -> 6.1 | Helped |
| Represent multiple objects in one network without transition regularization (ablation) | MVDream · Aligned 3D object generation | DIFT distance (% of object size): 30.1 -> 18.7 | Helped |
| Add transition plausibility regularization conditioned on an empty prompt (ablation) | A3D · Aligned 3D object generation | DIFT distance (% of object size): 18.7 -> 14.2 | Helped |
| Condition transition plausibility regularization on a blended prompt instead of an empty prompt (ablation) | A3D · Aligned 3D object generation | DIFT distance (% of object size): 14.2 -> 6.1 | Helped |
| Use a 2-layer MLP instead of a 1-layer MLP (ablation) | A3D · Aligned 3D object generation | DIFT distance (% of object size): 6.1 -> 13.5 | Hurt |
| Use a 3-layer MLP instead of a 1-layer MLP (ablation) | A3D · Aligned 3D object generation | DIFT distance (% of object size): 6.1 -> 14.3 | Hurt |
| Use A3D rather than editing-based competitors | Aligned 3D object generation · Aligned 3D object generation | CLIP similarity: 27.1 -> 27.7; 26.4 -> 27.7; 22.8 -> 27.7 | Helped |
| Use A3D rather than editing-based competitors | Structure-preserving transformation · Structure-preserving transformation | CLIP similarity: 27.9 -> 27.8; 24.6 -> 27.8 | Mixed |
Surprising findings
- Using an empty text prompt for the plausibility regularizer already substantially improves alignment (DIFT 14.2 vs 18.7), showing the gain is not solely from blending prompt embeddings.
- GaussianEditor and MVEdit achieve lower DIFT distance than A3D in generation, because they often fail to change the geometry of the initial object, yielding poor text alignment.
Limitations
- Limited to generating static aligned objects; cannot handle pose-changing tasks.
- Alignment comes with reduced diversity and stylization when prompts are naturally different (objects made more similar via interpolation).
- Scaling to more objects uses linear iteration heuristic without knowledge sharing; tested up to 5 objects.
Experimental setup
Metrics: GPTEval3D win rate CLIP similarity DIFT distance
Baselines: MVEdit LucidDreamer GaussianEditor MVDream
Builds on: MVDream DreamFusion Score Distillation Sampling StyleGAN trajectory regularization threestudio
Closest papers by meaning
- SweetDreamer: Aligning Geometric Priors in 2D diffusion for Consistent Text-to-3D (ICLR 2024 · similarity 0.89)
- Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation (ICLR 2024 · similarity 0.89)
- Retrieval-Augmented Score Distillation for Text-to-3D Generation (ICML 2024 · similarity 0.88)
- DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data (CVPR 2024 · similarity 0.88)
- Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors (CVPR 2024 · similarity 0.88)
- Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior (CVPR 2024 · similarity 0.88)
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