# Sparse Control

DreamMesh4D is a novel framework that combines mesh representation with sparse control deformation techniques to generate high-quality 4D objects from monocular videos. This technology addresses the challenges of spatial-temporal consistency and surface texture quality seen in traditional methods by integrating implicit neural radiance fields (NeRF) or explicit Gaussian drawing as underlying representations. Drawing inspiration from modern 3D animation workflows, DreamMesh4D binds Gaussian drawing to triangle mesh surfaces, enabling differentiable optimization of textures and mesh vertices. The framework starts with a rough mesh provided by single-image 3D generation methods and constructs a deformation graph by uniformly sampling sparse points to enhance computational efficiency while providing additional constraints. Through two-stage learning, it leverages reference view photometric loss, score distillation loss, and other regularization losses to effectively learn static surface Gaussians, mesh vertices, and dynamic deformation networks. DreamMesh4D outperforms previous video-to-4D generation methods in rendering quality and spatial-temporal consistency, and its mesh-based representation is compatible with modern geometric processes, showcasing its potential in the 3D gaming and film industries.

SparseCtrl is developed to enhance the controllability of text-to-video generation. It enables flexible structural control by combining sparse signals with only one or a few inputs. It includes an additional conditional encoder to process these sparse signals without affecting the pre-trained text-to-video model. This method is compatible with various formats, including sketches, depth, and RGB images, providing more practical control for video generation and pushing applications like storyboarding, depth rendering, keyframe animation, and interpolation. Extensive experiments demonstrate SparseCtrl's generalization ability on both original and personalized text-to-video generators.