Ctrl-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion
Bibliographic record
Abstract
Achieving controllable and consistent editing in dynamic 3D scenes remains a significant challenge. Previous work is largely constrained by its editing backbones, resulting in inconsistent edits and limited controllability. We propose to address this challenge using personalized diffusion models. In our work, we introduce a novel framework that first fine-tunes the InstructPix2Pix model, followed by a two-stage optimization of the scene based on deformable 3D Gaussians. The fine-tuning enables the model to "learn" the editing ability from a single edited reference image. This reduces the more complex task of dynamic scene editing to the more simple 2D image editing problem. By directly learning editing regions and styles from the reference, our approach enables consistent and precise local edits without the need for tracking desired editing regions, effectively addressing key challenges in dynamic scene editing. Then, our two-stage optimization progressively edits the trained dynamic scene, using a designed edited image buffer to accelerate convergence and improve temporal consistency. Compared to state-of-the-art methods, our approach offers more flexible and controllable local scene editing, achieving high-quality and consistent results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 0.001 |
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".