Search Results for author:
Found 8 papers, 6 papers with code
MegActor-$Σ$: Unlocking Flexible Mixed-Modal Control in Portrait Animation with Diffusion Transformer
To address this issue, we introduce MegActor-$\Sigma$: a mixed-modal conditional diffusion transformer (DiT), which can flexibly inject audio and visual modality control signals into portrait animation.
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation
Transformer-based deep models for single image super-resolution (SISR) have greatly improved the performance of lightweight SISR tasks in recent years.
Large Kernel Distillation Network for Efficient Single Image Super-Resolution
Efficient and lightweight single-image super-resolution (SISR) has achieved remarkable performance in recent years.
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition
For evaluating the task, we construct a new benchmark, Something-composition (Sth-com), based on the widely used Something-Something V2 dataset.
MegActor: Harness the Power of Raw Video for Vivid Portrait Animation
Despite raw driving videos contain richer information on facial expressions than intermediate representations such as landmarks in the field of portrait animation, they are seldom the subject of research.
TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object Tracking
There is currently strong interest in improving visual object tracking by augmenting the RGB modality with the output of a visual event camera that is particularly informative about the scene motion.
FAAC: Facial Animation Generation with Anchor Frame and Conditional Control for Superior Fidelity and Editability
Our method's efficacy has been validated on multiple representative DreamBooth and LoRA models, delivering substantial improvements over the original outcomes in terms of facial fidelity, text-to-image editability, and video motion.
Efficient One Pass Self-distillation with Zipf's Label Smoothing
This paper proposes an efficient self-distillation method named Zipf's Label Smoothing (Zipf's LS), which uses the on-the-fly prediction of a network to generate soft supervision that conforms to Zipf distribution without using any contrastive samples or auxiliary parameters.
