DreamPet: Text Driven Controllable 3D Animal Generation using Gaussian Splatting
Bibliographic record
Abstract
Realistic 3D animal generation from text prompts is a significant yet challenging task. Traditional approaches, which use score distillation sampling to optimize 3D formats like meshes or neural fields, often suffer from a lack of detail and designed for fixed shape. To address both limitations, in this work, we introduce DreamPet, a novel framework that explores a retrieval-augmented approach tailored for score distillation and efficiently produces high-quality 3D animal models featuring fine-grained geometry and lifelike textures. Our key insight is that both expressiveness of 2D diffusion models and geometric consistency of 3D animal assets can be fully leveraged by employing the semantically relevant assets directly within the optimization process. Specifically, our method features 1) a Shape-Aware SDS for optimizing appearance and geometry to ensure structural consistency per category, and 2) a Category aware refinement module that addresses the over-saturation issue and further eliminates floating artefacts based on the animal category to produce realistic textures. Extensive experiments demonstrate competitive quality of our method, rendering 3D animals under diverse scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".