BFS is a dual-branch diffusion model with bidirectional knowledge transfer that synthesizes coherent foreground layers with visual effects using a two-stage training scheme on unlayered data.
Layerfusion: Harmonized multi-layer text-to-image generation with generative priors
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
LASAGNA produces layered images with integrated visual effects in a single pass, enabling drift-free edits via alpha compositing while releasing a 48K dataset and a 242-sample benchmark.
UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.
citing papers explorer
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BFS: Back-to-Front Layered Image Synthesis via Knowledge Transfer
BFS is a dual-branch diffusion model with bidirectional knowledge transfer that synthesizes coherent foreground layers with visual effects using a two-stage training scheme on unlayered data.
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A Unified and Controllable Framework for Layered Image Generation with Visual Effects
LASAGNA produces layered images with integrated visual effects in a single pass, enabling drift-free edits via alpha compositing while releasing a 48K dataset and a 242-sample benchmark.
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UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors
UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.