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Found 10 papers, 5 papers with code
OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics Generation
Comprehensive experiments demonstrate that OmniPhysGS achieves more general and realistic physical dynamics across a broader spectrum of materials, including elastic, viscoelastic, plastic, and fluid substances, as well as interactions between different materials.
MVPortrait: Text-Guided Motion and Emotion Control for Multi-view Vivid Portrait Animation
Furthermore, by leveraging FLAME as a bridge, MVPortrait becomes the first controllable portrait animation framework that is compatible with text, speech, and video as driving signals.
Training-free Regional Prompting for Diffusion Transformers
Diffusion models have demonstrated excellent capabilities in text-to-image generation.
Generalizable Human Gaussians for Sparse View Synthesis
To tackle this challenge, this paper leverages recent advancements in Gaussian Splatting and introduces a new method to learn generalizable human Gaussians that allows photorealistic and accurate view-rendering of a new human subject from a limited set of sparse views in a feed-forward manner.
Personalized Face Inpainting with Diffusion Models by Parallel Visual Attention
Specifically, we insert parallel attention matrices to each cross-attention module in the denoising network, which attends to features extracted from reference images by an identity encoder.
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
By using ~40 reference images, PATMAT creates anchor points in MAT's style module, and tunes the model using the fixed anchors to adapt the model to a new face identity.
Extracting Semantic Knowledge from GANs with Unsupervised Learning
Second, we train image-to-image translation networks on the synthesized datasets, enabling semantic-conditional image synthesis without human annotations.
Bridging the Gap Between Training and Inference of Bayesian Controllable Language Models
Large-scale pre-trained language models have achieved great success on natural language generation tasks.
Linear Semantics in Generative Adversarial Networks
Given a trained GAN and as few as eight semantic annotations, the user is able to generate diverse images subject to a user-provided semantic layout, and control the synthesized image semantics.
Frame Difference-Based Temporal Loss for Video Stylization
To ensure temporal inconsistency between the frames of the stylized video, a common approach is to estimate the optic flow of the pixels in the original video and make the generated pixels match the estimated optical flow.
