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Found 7 papers, 7 papers with code
MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation
AToM excels in capturing subtle lip movements by leveraging an audio attention mechanism.
DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization
The objective of text-to-image (T2I) personalization is to customize a diffusion model to a user-provided reference concept, generating diverse images of the concept aligned with the target prompts.
Diffusion Model for Dense Matching
The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term.
DiffFace: Diffusion-based Face Swapping with Facial Guidance
In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending.
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters.
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
However, the tokenization of a correlation map for transformer processing can be detrimental, because the discontinuity at token boundaries reduces the local context available near the token edges and decreases inductive bias.
Ranked #2 on
Semantic correspondence
on PF-WILLOW
Cost Aggregation Is All You Need for Few-Shot Segmentation
We introduce a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support.
Ranked #3 on
Semantic correspondence
on PF-WILLOW
