CineMatte uses a cross-attention design on a Siamese DINOv3 ViT plus a pretrained upsampler to produce robust mattes for virtual production, backed by a new non-synthetic 4K VP dataset that supports camera motion.
Deep automatic natural image matting.arXiv preprint arXiv:2107.07235
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SAM2Matting decouples tracking from matting to deliver SOTA video matting performance using models trained only on images.
MagicBokeh uses a single diffusion model with alternative training, focus-aware masked attention, and degradation-aware depth estimation to produce photorealistic bokeh on low-res zoomed images.
citing papers explorer
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CineMatte: Background Matting for Virtual Production and Beyond
CineMatte uses a cross-attention design on a Siamese DINOv3 ViT plus a pretrained upsampler to produce robust mattes for virtual production, backed by a new non-synthetic 4K VP dataset that supports camera motion.
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SAM2Matting: Generalized Image and Video Matting
SAM2Matting decouples tracking from matting to deliver SOTA video matting performance using models trained only on images.
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Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework
MagicBokeh uses a single diffusion model with alternative training, focus-aware masked attention, and degradation-aware depth estimation to produce photorealistic bokeh on low-res zoomed images.