Search Results for author:
Found 11 papers, 7 papers with code
TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models
To guide video generation with the additional image input, we propose a "repeat-and-slide" strategy that modulates the reverse denoising process, allowing the frozen diffusion model to synthesize a video frame-by-frame starting from the provided image.
3D-Aware Talking-Head Video Motion Transfer
In this paper, we propose a novel 3D-aware talking-head video motion transfer network, Head3D, which fully exploits the subject appearance information by generating a visually-interpretable 3D canonical head from the 2D subject frames with a recurrent network.
Synthetic Augmentation with Large-scale Unconditional Pre-training
To further reduce the dependency on annotated data, we propose a synthetic augmentation method called HistoDiffusion, which can be pre-trained on large-scale unlabeled datasets and later applied to a small-scale labeled dataset for augmented training.
Exploring Compositional Visual Generation with Latent Classifier Guidance
We show that this paradigm based on latent classifier guidance is agnostic to pre-trained generative models, and present competitive results for both image generation and sequential manipulation of real and synthetic images.
Conditional Image-to-Video Generation with Latent Flow Diffusion Models
In this paper, we propose an approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image.
Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment
We collected a new clinical IMV dataset with GMA annotations, and our experiments show that SPN models for body parsing and pose estimation trained on the first two datasets generalize well to the new clinical dataset and their results can significantly boost the CRNN-based GMA prediction performance.
Cross-identity Video Motion Retargeting with Joint Transformation and Synthesis
The novel design of dual branches combines the strengths of deformation-grid-based transformation and warp-free generation for better identity preservation and robustness to occlusion in the synthesized videos.
Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans
In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs for more effective and interpretable AIS segmentation.
DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning
In an emergency room (ER) setting, stroke triage or screening is a common challenge.
SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos
In this paper, we propose a semi-supervised body parsing model, termed SiamParseNet (SPN), to jointly learn single frame body parsing and label propagation between frames in a semi-supervised fashion.
Action Recognition Based on Optimal Joint Selection and Discriminative Depth Descriptor
This paper proposes a novel human action recognition using the decision-level fusion of both skeleton and depth sequence.
