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Versatile Vision Foundation Model for Image and Video Colorization

Versatile Vision Foundation Model for Image and Video Colorization

by America Ortiz | Jul 28, 2024 | Rendering, Video Processing, Visual Computing

Versatile Vision Foundation Model for Image and Video Colorization In this work we show how a latent diffusion model, pre-trained on text-to-image synthesis, can be finetuned for image colorization and provide a flexible solution for a wide variety of scenarios: high...
Controllable Neural Style Transfer for Dynamic Meshes

Controllable Neural Style Transfer for Dynamic Meshes

by America Ortiz | Jul 28, 2024 | Animation, Visual Computing

Controllable Neural Style Transfer for Dynamic Meshes In this paper we propose a novel mesh stylization technique that improves previous NST works in several ways. First, we replace the standard Gram-Matrix style loss by a Neural Neighbor formulation that enables...
Neural Denoising for Deep-Z Monte Carlo Renderings

Neural Denoising for Deep-Z Monte Carlo Renderings

by America Ortiz | Jun 18, 2024 | Rendering, Video Processing, Visual Computing

Neural Denoising for Deep-Z Monte Carlo Renderings We propose a hybrid reconstruction architecture that combines the depth-resolved reconstruction at each bin with the flattened reconstruction at the pixel level. April 20, 2024 Eurographics (2024)       Authors...
DiVAS: Video and Audio Synchronization with Dynamic Frame Rates

DiVAS: Video and Audio Synchronization with Dynamic Frame Rates

by America Ortiz | Jun 17, 2024 | Video Processing, Visual Computing

DiVAS: Video and Audio Synchronization with Dynamic Frame Rates In this paper, we study the automatic discovery of such issues. Specifically, we focus on the alignment of lip movements with spoken words, targeting realistic production scenarios which can include...
Combining Frame and GOP Embeddings for Neural Video Representation

Combining Frame and GOP Embeddings for Neural Video Representation

by America Ortiz | Jun 17, 2024 | Video Processing, Visual Computing

Combining Frame and GOP Embeddings for Neural Video Representation In this paper, we propose T-NeRV, a hybrid video INR that combines frame-specific embeddings with GOP-specific features, providing a lever for content-specific fine-tuning. June 17, 2024 CVPR (2024)...
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