We introduce HDEdit, a training-free framework for instruction-guided video and 3D scene editing that resolves the fundamental tension between instruction fulfillment and original content preservation through Hierarchical task Decomposition. Our key insight is to progressively decompose complex edits into simpler subtasks. This hierarchical strategy aligns with dual objectives: an LLMguided planner structures high-level subgoals for reliable instruction fulfillment, while embedding-space interpolation further refines each subgoal to preserve unedited content. Two tailored control mechanisms - word-level attention map propagation and parallel denoising synchronization - ensure temporally consistent, hyperparameter tuning-free execution. Beyond video, we extend HDEdit to 3D editing via a simple yet effective render-edit-reconstruct process that maintains strong geometric consistency. Extensive experiments demonstrate our state-of-the-art results across diverse and challenging edits, including longduration videos, fast camera motion, and significant 3D geometric changes.

This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite their impressive results on novel-view synthesis, it remains a fundamental challenge for NeRFs to edit the shape of the scene. Our key insight is to exploit the explicit point cloud representation as the underlying structure to construct NeRFs, inspired by the intuitive interpretation of NeRF rendering as a process that projects or “plots” the associated 3D point cloud to a 2D image plane. To this end, NeuralEditor introduces a novel rendering scheme based on deterministic integration within K-D tree-guided density-adaptive voxels, which produces both high-quality rendering results and precise point clouds through optimization. NeuralEditor then performs shape editing via mapping associated points between point clouds. Extensive evaluation shows that NeuralEditor achieves state-of-the-art performance in both shape deformation and scene morphing tasks. Notably, NeuralEditor supports both zeroshot inference and further fine-tuning over the edited scene. Our code, benchmark, and demo video are available at immortalco.github.io/NeuralEditor.