SemantiScene-Edit: Expert-Synthesized Supervision and Dual-Path Source Conditioning for

We also construct a 100-case benchmark for evaluating instruction execution, source preservation, temporal consistency, and visual quality. It comes from a paper posted to arXiv on 31 October 2026. Instruction-guided video editing requires executing a requested change while preserving the source video's appearance and temporal structure. We present SemantiScene-Edit, a video editor that couples a multimodal encoder with a pretrained video diffusion transformer. The encoder jointly processes source frames and the editing instruction, and learnable queries convey their semantic context to the generator through a trainable connector and cross-attention. A complementary visual pathway concatenates source-video latents with noisy target latents before patch embedding, supplying dense spatial and temporal information for content preservation. We train the model with flow matching and a three-stage image--video curriculum comprising semantic alignment, joint adaptation, and high-resolution refinement. To support training, we construct over 1.2 million instruction-conditioned video-editing records covering object addition, removal, replacement, and attribute modification. Comparative experiments show that SemantiScene-Edit effectively follows editing instructions while retaining source content. At inference, the model requires only a source video and a natural-language instruction.

Why it counts

We also construct a 100-case benchmark for evaluating instruction execution, source preservation, temporal consistency, and visual quality.

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