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Found 7 papers, 6 papers with code
DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT
However, prior works tend to produce unsatisfactory results, as the classic GPT framework is designed to handle 1D contextual information, such as text, and lacks the inherent ability to model the spatial and temporal dynamics essential for video generation.
Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration
Monocular camera calibration is essential for many 3D vision tasks.
Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution
The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales.
Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution
The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual quality of videos, by simultaneously performing video frame interpolation (VFI) and video super-resolution (VSR).
Space-time Video Super-resolution
Video Frame Interpolation
+1
Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints
Next, the Appearance Generation Stage employs a fine-tuned ControlNet to produce a vivid panoramic image of the room guided by the 3D scene layout and text prompt.
A Dynamic Multi-Scale Voxel Flow Network for Video Prediction
The performance of video prediction has been greatly boosted by advanced deep neural networks.
Ranked #1 on
Video Prediction
on Cityscapes
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch.
