# ESA: Energy-Based Shot Assembly Optimization for Automatic Video Editing

arXiv: https://arxiv.org/abs/2511.02505 · HTML: https://mlsanity.com/paper/2511.02505/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [How to Train Your Energy-Based Models](https://mlsanity.com/paper/2101.03288/) (2021)
- [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://mlsanity.com/paper/2211.06679/) (2022)
- [Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement](https://mlsanity.com/paper/2304.14391/) (2023)
- [Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2306.09869/) (2023)
- [Cinematographic Camera Diffusion Model](https://mlsanity.com/paper/2402.16143/) (2024)
- [Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2404.07389/) (2024)
- [TVG: A Training-free Transition Video Generation Method with Diffusion Models](https://mlsanity.com/paper/2408.13413/) (2024)
- [NLUT: Neural-based 3D Lookup Tables for Video Photorealistic Style Transfer](https://mlsanity.com/paper/2303.09170/) (2023)
- [MVOC: a training-free multiple video object composition method with diffusion models](https://mlsanity.com/paper/2406.15829/) (2024)
- [HyperLips: Hyper Control Lips with High Resolution Decoder for Talking Face Generation](https://mlsanity.com/paper/2310.05720/) (2023)
- [VCoME: Verbal Video Composition with Multimodal Editing Effects](https://mlsanity.com/paper/2407.04697/) (2024)
- [NCST: Neural-based Color Style Transfer for Video Retouching](https://mlsanity.com/paper/2411.00335/) (2024)

## Built on by (most cited first, 2 of 2)

- [DIRECT: Video Mashup Creation via Hierarchical Multi-Agent Planning and Intent-Guided Editing](https://mlsanity.com/paper/2604.04875/) (2026)
- [CutClaw: Agentic Hours-Long Video Editing via Music Synchronization](https://mlsanity.com/paper/2603.29664/) (2026)

## Abstract (arXiv)

Shot assembly is a crucial step in film production and video editing, involving the sequencing and arrangement of shots to construct a narrative, convey information, or evoke emotions. Traditionally, this process has been manually executed by experienced editors. While current intelligent video editing technologies can handle some automated video editing tasks, they often fail to capture the creator's unique artistic expression in shot assembly. To address this challenge, we propose an energy-based optimization method for video shot assembly. Specifically, we first perform visual-semantic matching between the script generated by a large language model and a video library to obtain subsets of candidate shots aligned with the script semantics. Next, we segment and label the shots from reference videos, extracting attributes such as shot size, camera motion, and semantics. We then employ energy-based models to learn from these attributes, scoring candidate shot sequences based on their alignment with reference styles. Finally, we achieve shot assembly optimization by combining multiple syntax rules, producing videos that align with the assembly style of the reference videos. Our method not only automates the arrangement and combination of independent shots according to specific logic, narrative requirements, or artistic styles but also learns the assembly style of reference videos, creating a coherent visual sequence or holistic visual expression. With our system, even users with no prior video editing experience can create visually compelling videos. Project page: https://sobeymil.github.io/esa.com