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Found 8 papers, 1 papers with code
FastDrag: Manipulate Anything in One Step
Drag-based image editing using generative models provides precise control over image contents, enabling users to manipulate anything in an image with a few clicks.
Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer
In this work, we introduce Direct3D, a native 3D generative model scalable to in-the-wild input images, without requiring a multiview diffusion model or SDS optimization.
STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians
Recent progress in pre-trained diffusion models and 3D generation have spurred interest in 4D content creation.
UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation
Recent advancements in text-to-3D generation technology have significantly advanced the conversion of textual descriptions into imaginative well-geometrical and finely textured 3D objects.
Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle
Moreover, the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene, which is comparable to the original 3DGS designed for static 3D reconstruction.
Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing
In this paper, we present a novel differentiable point-based rendering framework to achieve photo-realistic relighting.
EARL: An Elliptical Distribution aided Adaptive Rotation Label Assignment for Oriented Object Detection in Remote Sensing Images
In addition, a dynamic elliptical distribution aided sampling (DED) strategy is proposed to make the sample distribution more flexible to fit the shapes and orientations of targets, and filter out low-quality samples.
IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection
First, a novel geometric transformation is employed to better represent the oriented object in angle prediction, then a branch interactive module with a self-attention mechanism is developed to fuse features from classification and box regression branches.
