---
title: 'Video CoM: Diverse Video Manipulation Methods'
url: https://www.emergentmind.com/topics/video-com
type: topic
---

# Video CoM: Diverse Video Manipulation Methods

In current arXiv usage, “Video CoM” is not a single canonical task but an overloaded label spanning several nearby research programs. The term appears most directly in **interactive video reasoning via a Chain of Manipulations**, but adjacent literature also uses closely related names for **composed video retrieval**, **multimodal video composition/editing**, **video token compaction and cross-modality coding**, and **common/unique or motion-content factorization** [2511.23477][2506.05274][2407.04697][2505.14454][2410.01718]. A plausible unifying theme is the replacement of one-shot monolithic video processing by structured operations over temporal segments, frames, spatial regions, multimodal side information, or decomposed latent factors.

## 1. Terminological scope

The most explicit usage is **“Video-CoM: Interactive Video Reasoning via Chain of Manipulations”**, where CoM denotes a reasoning policy that repeatedly manipulates the video during inference [2511.23477]. However, neighboring papers use “CoM,” “COM,” “Com,” or similar abbreviations for substantially different objects: composed retrieval, composition/editing, compression, common-signal decomposition, or even unrelated benchmark names.

| Usage | Core idea | Representative papers |
|---|---|---|
| Interactive reasoning | Manipulate video while reasoning | [2511.23477], [2310.10586] |
| Composed retrieval | Retrieve a target video from source video + modification text | [2506.05274], [2508.14039] |
| Video composition/editing | Plan or generate edits/effects/compositions | [2407.04697], [2409.10090], [2406.15829] |
| Compression/coding | Compact video tokens or code video via multimodal priors | [2505.14454], [2408.08093], [2107.12192] |
| Common/unique or motion coupling | Factorize persistent versus varying video signals | [2410.01718], [2601.10632], [2605.22996] |

This dispersion is not merely terminological. Some papers treat CoM as a **reasoning policy**, some as a **retrieval composition operator**, some as **video editing or compositing**, and some as **compression or factorization**. This suggests that “Video CoM” is best read contextually rather than as a stable field-wide acronym.

## 2. Composed video retrieval

In retrieval-oriented usage, Video CoM is most closely aligned with **Composed Video Retrieval (CoVR)**. TF-CoVR defines retrieval over triplets \((V_q, T_m, V_t)\), where \(V_q\) is a query video, \(T_m\) is a modification text, and \(V_t\) is the target video; the pair \(\langle V_q, T_m\rangle\) is the composed query [2506.05274]. The paper emphasizes **temporally fine-grained** modifications in gymnastics and diving, rather than appearance-only edits. Its benchmark contains **180K training triplets**, **473 test queries**, **306 fine-grained sports actions**, and **3.94 valid targets on average** per test query, which is why it evaluates with **mAP@5/10/25/50** rather than only Recall@K [2506.05274].

The proposed **TF-CoVR-Base** uses a two-stage design: pre-train **AIM** on **306** action classes for temporally discriminative video embeddings, then align composed queries and target videos with contrastive learning. Quantitatively, it improves zero-shot **mAP@50** from **5.92** to **7.51**, and after fine-tuning raises the state of the art from **19.83** to **25.82** [2506.05274]. The paper’s main substantive claim is that fine-grained CoVR depends on temporal action semantics such as twist count, somersault count, direction, and apparatus, and that generic caption-style pipelines fail to preserve these distinctions.

A larger-scale continuation appears in **“Beyond Simple Edits: Composed Video Retrieval with Dense Modifications”**, which introduces **Dense-WebVid-CoVR** with **1.6 million samples** and much denser relative descriptions [2508.14039]. The benchmark reports average description length **81.32 words** and average modification length **31.16 words**, versus **6.68** and **4.6** in WebVid-CoVR, respectively. The proposed model uses **Cross-Attention fusion** with a grounded text encoder and reaches **71.3\% Recall@1** in the visual+text setting, outperforming the prior state of the art by **3.4\%** [2508.14039]. A common misconception in this area is to treat CoVR as ordinary text-video retrieval; the cited formulations instead require a **relative transformation** from a source video to a target video.

## 3. Multimodal composition and editing

A second major usage concerns **video composition** in the editing sense. **VCoME** formulates **verbal video composition with multimodal editing effects** as autoregressive generation over segment-indexed effect tuples \(y_i = \big(i, (l_1,e_1), (l_2,e_2), \dots \big)\), where \(l\) is a trigger position in the verbal content and \(e\) is an effect name [2407.04697]. Inputs are sentence-level multimodal segments \(x_i=\{s_i,v_i,a_i\}\), and the output effect vocabulary spans **text animation**, **text effect**, **text template**, **image stickers**, and **sound effects**. The dataset contains about **53,000 instances**, split into **51,000 training** and **2,100 validation** samples. With prompt control, the best reported main result is **138.03** overall, and the user study claims outputs of professional quality while being **85× more efficient than professional editors** [2407.04697]. This is planning and recommendation over an existing verbal video, not raw video generation from scratch.

**MotionCom** addresses a different composition regime: **training-free motion-aware image composition** using an LVLM for placement planning and a video diffusion prior for motion-infused synthesis [2409.10090]. It plans an insertion region \(R^*\) and split ratio \(S_{\mathrm{ratio}}\) with GPT-4V, constructs an intermediate composition, then applies **MotionPaint**, a masked latent blending procedure over a pretrained image-to-video model. The output is ultimately a **single selected frame** from a generated video rather than a full edited video. This matters because the method imports **video priors** into composition without becoming a full video-compositing pipeline.

**MVOC** is the closest to literal video object composition. It is a **training-free multiple video object composition** method that performs **DDIM inversion** on each source object video, composes a first frame with an image editing method, and then generates the composited video with a **Video Object Dependence Module** that injects object features and attention maps into an image-to-video model [2406.15829]. The method is designed to preserve **identity consistency**, **motion consistency**, and **temporal consistency** while allowing interaction effects among non-independent objects. On the reported benchmark, it achieves the best average short- and long-range warping errors, **0.0010** and **0.0014**, respectively [2406.15829]. A recurrent misconception in this literature is to equate video composition with framewise blending; these papers instead treat it as structured planning or conditioned generation over temporally coherent content.

## 4. Interactive reasoning and multimodal prompting

The most literal “Video-CoM” formulation is **interactive video reasoning via Chain of Manipulations**. At reasoning step \(i\), Video-CoM generates \(\mathcal{O}_i = f_{\text{MLLM}}(T_i,\mathcal{V}_i)\), where \(\mathcal{O}_i\) includes both reasoning tokens and a manipulation \(m_i\), and the next visual state is \(\mathcal{V}_{i+1}=g(\mathcal{V}_i,m_i)\) [2511.23477]. The three atomic manipulations are **find-segment**, **find-frame**, and **spatial-zoom**. The system is trained with **Video-CoM-Instruct**, an **18K** instruction-tuning dataset with **15K** SFT and **3K** GRPO samples, and then optimized with **reasoning-aware GRPO**, whose total reward is \(R = R_{\text{accuracy}} + R_{\text{reason}}\) [2511.23477]. The reported result is **68.7** on **Video-CoM-Bench** and an average gain of **3.6 percent** over recent state-of-the-art models, while using only **25K SFT** and **3K GRPO** video samples [2511.23477]. The paper’s defining claim is that video MLLMs should “think with videos,” not merely “think about videos.”

A related but tuning-free line is **VidCoM**, which converts selected video events into **scene graphs** and **descriptive captions**, then lets an LLM reason over those event representations [2310.10586]. Its **InsOVER** algorithm first initializes events by moving-average clustering over CLIP frame embeddings, then refines them by Hungarian matching between **OpenIE-decomposed instruction assertions** and **visual sub-events**. On STAR VideoQA, the ChatGPT-based variant reaches mean accuracy **50**, outperforming few-shot **Flamingo-80B**; on ActivityNet-Captions DVC, it reaches **3.7 SODA** under predicted proposals and **6.6 SODA** under true proposals [2310.10586]. Here again, the salient move is explicit event selection and iterative evidence focus, rather than treating the entire video as a fixed context.

In robotics, **Chain-of-Modality (CoM)** uses video plus **EMG or audio** and **hand pose** to extract manipulation plans from a single multimodal human demonstration [2504.13351]. The prompting order is force/audio, then hand pose, then image/video, and the outputs are skill sequences plus control parameters such as force intensity, direction, angle, and hand assignment. The paper reports **60% accuracy** for extracting exact task plans and control parameters, compared with **0%** for vision-only methods, and about **73%** average success in real-robot execution [2504.13351]. Although this is not the same task as Video-CoM [2511.23477], both papers replace static video interpretation with staged evidence gathering and intermediate control decisions.

## 5. Compression, coding, and compressed-domain inference

Another strong usage of “Video CoM” concerns **video compression or compaction**. **VidCom\(^2\)** is explicitly described as a **training-free, plug-and-play visual token compaction method for VideoLLMs** that operates entirely at inference time [2505.14454]. It does **not** compress pixels like a codec; it compresses the **visual token sequence** passed into the language model. Its pre-LLM operator first measures frame uniqueness, allocates per-frame retention ratios, and then keeps Top-\(K\) tokens within each frame according to a combined frame-local and video-global uniqueness score. On **LLaVA-OV-7B** at **25% visual token retention**, VidCom\(^2\) achieves **99.6%** of the original average performance while reducing **LLM generation latency by 70.8%** [2505.14454]. The paper’s three design principles are **Model Adaptability**, **Frame Uniqueness**, and **Operator Compatibility**, and it is explicit that the method is **token selection/pruning**, not token merging.

At the codec level, **Cross-Modality Video Coding (CMVC)** decomposes a video into keyframes \(K=\{k_1,\dots,k_n\}\) and inter-keyframe motion \(M=\{m_1,\dots,m_{n-1}\}\), maps them into alternative modalities via \(T_{k,i}=f(k_i)\) and \(T_{m,j}=g(m_j)\), and reconstructs with \(\hat V=\mathcal G(\hat K,\hat M)\) [2408.08093]. The paper instantiates **TT2V** (Text-Text-to-Video) for semantic reconstruction and **IT2V** (Image-Text-to-Video) for perceptual consistency. TT2V is framed as effective for **semantic reconstruction** at ultra-low bitrate, while IT2V is reported to achieve competitive perceptual consistency, including a **BD-rate** of **-65.71** on **Class B** under **DISTS** in one configuration [2408.08093]. The motion side is chiefly represented as **textual descriptions**, not classical motion vectors.

A compressed-domain inference counterpart is **CoVOS**, which accelerates semi-supervised VOS by using **frame types**, **motion vectors**, and **residuals** from the compressed bitstream [2107.12192]. It runs the heavy base VOS model only on **I- and P-frames**, then propagates masks to non-keyframes with bi-directional, multi-hop motion-vector warping and selectively corrects failures in high-residual regions. The paper reports speed-ups of up to **3.5X** with minor drops in accuracy across DAVIS17 and YouTube-VOS, and on DAVIS17 **STCN + CoVOS** reaches **82.4 \(J{data}F\)** at **33.7 FPS** [2107.12192]. A common misconception is to equate compressed-domain video reasoning with simply decoding RGB first; these papers instead treat the **bitstream structure itself** as a computational prior.

## 6. Factorized generative uses and non-equivalent adjacent meanings

Some “CoM/COM” papers are about **factorizing persistent and varying video structure** rather than retrieval, reasoning, or compression. **COMUNI** decomposes video into **common** and **unique** signals using **CU-VAE** and generates with **CU-LDM** [2410.01718]. The supplied appendix emphasizes that keeping conditional latents fixed during iterative generation yields the best long-video behavior; among the tested strategies, fixing all conditional latent features gives the best reported **FVD**, **6032.1**, and the best content consistency [2410.01718]. Here, the “common” component corresponds to temporally shared content, while the “unique” component carries frame-specific variation.

**CoMoVi** pushes factorization into explicit joint generation of **3D human motion** and **RGB video** [2601.10632]. It couples two video diffusion branches with mutual feature interaction and 3D-2D cross-attention, using a rendered 2D human motion representation to inherit pretrained VDM priors. On the CoMoVi Dataset test set it reports **FID 0.349**, **R@1 0.565**, **R@3 0.640**, and **MMDist 3.035** for motion generation, while also improving several VBench video metrics over Wan2.2-I2V-5B [2601.10632]. In a related controllable-generation direction, **CoMoGen** takes a single input image and a **binary mask sequence**, identifies **Motion Layers** in MMDiT, and fine-tunes only those layers with LoRA; the added parameter cost is reported as **1.84%** at inference and **3.17%** during training [2605.22996].

Other acronym-nearby usages are even less directly related. **COM Kitchens** is a benchmark name rather than a CoM method; it introduces **Online Recipe Retrieval (OnRR)** and **Dense Video Captioning on unedited Overhead-View videos (DVC-OV)** on **145 annotated videos / 40 hours** collected from **70 kitchens** and **70 unique actors** [2408.02272]. Outside video-language modeling, **CoM** may simply mean **center of mass**, as in 3D biped balance control with ZMP and CoM height variation [1709.06456], and **Comyco** denotes a quality-aware ABR controller that optimizes a VMAF-based QoE via imitation learning rather than any video reasoning or composition task [1908.02270]. This suggests that acronym overlap should not be treated as conceptual equivalence: in current literature, “Video CoM” names a family of distinct, only partly overlapping ideas rather than a single research problem.

Source: https://www.emergentmind.com/topics/video-com