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Found 14 papers, 7 papers with code
Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model
This technical report highlights the key design decisions that enhance the performance of the medium-sized diffusion model.
Learning Feature-Preserving Portrait Editing from Generated Pairs
Portrait editing is challenging for existing techniques due to difficulties in preserving subject features like identity.
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation.
3DAvatarGAN: Bridging Domains for Personalized Editable Avatars
Finally, we propose a novel inversion method for 3D-GANs linking the latent spaces of the source and the target domains.
Video2StyleGAN: Disentangling Local and Global Variations in a Video
Image editing using a pretrained StyleGAN generator has emerged as a powerful paradigm for facial editing, providing disentangled controls over age, expression, illumination, etc.
CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions
The success of StyleGAN has enabled unprecedented semantic editing capabilities, on both synthesized and real images.
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks
The input to our method is trained GAN that can produce images in domain A and a single reference image I_B from domain B.
Flow-Guided Video Inpainting with Scene Templates
We consider the problem of filling in missing spatio-temporal regions of a video.
Barbershop: GAN-based Image Compositing using Segmentation Masks
Seamlessly blending features from multiple images is extremely challenging because of complex relationships in lighting, geometry, and partial occlusion which cause coupling between different parts of the image.
Labels4Free: Unsupervised Segmentation using StyleGAN
We propose an unsupervised segmentation framework for StyleGAN generated objects.
Improved StyleGAN Embedding: Where are the Good Latents?
First, we introduce a new normalized space to analyze the diversity and the quality of the reconstructed latent codes.
Channel-Directed Gradients for Optimization of Convolutional Neural Networks
We introduce optimization methods for convolutional neural networks that can be used to improve existing gradient-based optimization in terms of generalization error.
StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows
We evaluate our method using the face and the car latent space of StyleGAN, and demonstrate fine-grained disentangled edits along various attributes on both real photographs and StyleGAN generated images.
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e. g., we can specify one style reference image per region.
