LottieGPT tokenizes Lottie animations into compact sequences and fine-tunes Qwen-VL to autoregressively generate coherent vector animations from natural language or visual prompts, outperforming prior SVG models.
Adding conditional control to text-to-image diffusion models
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A diffusion model trained on synthetically damaged teeth from public datasets completes crowns with 81.8% IoU and 0.00034 Chamfer distance, and produces real-world restorations with minimal opposing-tooth interference.
citing papers explorer
-
LottieGPT: Tokenizing Vector Animation for Autoregressive Generation
LottieGPT tokenizes Lottie animations into compact sequences and fine-tunes Qwen-VL to autoregressively generate coherent vector animations from natural language or visual prompts, outperforming prior SVG models.
-
From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion
A diffusion model trained on synthetically damaged teeth from public datasets completes crowns with 81.8% IoU and 0.00034 Chamfer distance, and produces real-world restorations with minimal opposing-tooth interference.