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arxiv: 2606.23327 · v1 · pith:UBUALROJnew · submitted 2026-06-22 · 💻 cs.CV · cs.AI

VideoAgent: All-in-One Framework for Video Understanding and Editing

Hengji Zhou , Lingxuan Huang , Jian Wang , Bing Zhou , Si Wu , Lianghao Xia , Chao Huang This is my paper

Pith reviewed 2026-06-26 09:20 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords video editingmulti-agent orchestrationvideo understandingshot planningagentic frameworkintent parsingtextual-gradient optimizationlong video processing
0
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The pith

VideoAgent orchestrates over thirty specialized agents into coherent long-video editing pipelines using intent parsing and textual-gradient optimization.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents VideoAgent as a single framework that handles both video understanding and editing for long, diverse content where prior automated tools were limited to short clips or narrow tasks. It introduces shot planning agents plus cross-modal retrieval to build aligned narratives, then routes user requests through a multi-agent system that selects and connects the right tools without manual pipeline design. Experiments on a new benchmark and public datasets report high rates of successful orchestration, lower API usage, and human-rated output that sits close to professionally made videos. A reader would care if this means complex video work can shift from specialized software and hours of editing to an automated process that scales across categories.

Core claim

VideoAgent is an all-in-one agentic framework that addresses the inability of existing systems to manage diverse video operations and long-form narrative coherence. It achieves this through automated video shot creation via shot planning agents and cross-modal retrieval for aligned visuals, paired with a multi-agent orchestration layer that uses intent parsing to filter relevant tools and textual-gradient graph optimization to assemble pipelines from more than thirty specialized editing agents.

What carries the argument

The multi-agent orchestration framework that integrates intent parsing to select tools and textual-gradient graph optimization to assemble complex editing pipelines from over thirty specialized agents.

If this is right

  • Long videos can be edited into coherent narratives rather than processed only as short independent segments.
  • Complex editing pipelines for varied tasks can be assembled automatically from a large pool of specialized agents.
  • API costs for video editing systems can drop substantially while maintaining high success in tool selection and assembly.
  • Output quality can reach levels rated only slightly below human-created videos across multiple content categories.
  • The system outperforms prior multimodal LLMs and agent-based approaches on both the introduced VideoEdit benchmark and existing datasets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar agent orchestration could be tested on sequential media such as audio tracks or image sequences to check whether the same planning and optimization steps transfer.
  • Running the framework on videos longer than those in the current benchmark would reveal whether narrative coherence holds or breaks at greater scale.
  • Adding iterative user feedback loops into the orchestration step might reduce the remaining quality gap to human editors without raising costs much.
  • The reported cost reductions could enable smaller production teams to generate professional-grade videos that previously required larger budgets or specialists.

Load-bearing premise

The multi-agent orchestration with intent parsing and textual-gradient graph optimization can reliably select and assemble pipelines from many specialized agents for diverse long-video tasks without producing narrative incoherence.

What would settle it

Long videos containing complex multi-shot stories where VideoAgent outputs show visible story breaks, mismatched shots, or require extensive manual fixes to become usable.

Figures

Figures reproduced from arXiv: 2606.23327 by Bing Zhou, Chao Huang, Hengji Zhou, Jian Wang, Lianghao Xia, Lingxuan Huang, Si Wu.

Figure 1
Figure 1. Figure 1: Automated video editing with VideoAgent. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Automated video shot creation with shot planning, video retrieval and trimming. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Video editing with agent graph orchestration and execution. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Human-rated video quality assessment. VideoAgent w/o Shot Plan w/o CM Rep. 0 10 20 30 40 50 Score Recall EMScore IoU [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: VideoAgent ablation study results. curacy impact, costs increase notably, confirm￾ing its cost-efficiency. Key Agent Dependencies. Ablating individual agents (LoudnessNormalizer (LN), AudioExtractor (AE), StandUpSynth (SS)) reveals proportional performance drops correlated with graph centrality, demonstrating effective multi￾agent orchestration even in complex scenarios. 3.4 Hyperparameter Study (RQ3) This… view at source ↗
Figure 6
Figure 6. Figure 6: Hyperparameter study for VideoAgent. accuracy, precision, recall, and F1 against human judgments. Results are shown in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Consistency study on LLM self-evaluation [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Case study: Creating a rhythm-synced Spiderman movie montage with VideoAgent. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Video cases of VideoAgent in real-world scenarios - Case 1 [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Video cases of VideoAgent in real-world scenarios - Case 2 [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Video cases of VideoAgent in real-world scenarios - Case 3 [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 13
Figure 13. Figure 13: Evaluation video captions/queries distribu [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗
read the original abstract

Video editing has become essential in digital media creation, yet existing automated systems are restricted to short segment processing and domain-specific tasks. They face two critical limitations: i) inability to handle diverse video comprehension and editing operations, and ii) lack of long-video understanding for coherent narrative creation. We propose VideoAgent, an all-in-one agentic framework addressing these challenges through two key innovations. First, we develop automated video shot creation with shot planning agents for coherent narratives and cross-modal retrieval for aligned visual content. Second, we design a multi-agent orchestration framework integrating over thirty specialized editing agents. Intent parsing filters relevant tools while textual-gradient graph optimization assembles complex editing pipelines. Extensive experiments on our newly-proposed VideoEdit benchmark and public datasets demonstrate VideoAgent's superiority over existing multimodal LLMs and agentic systems. VideoAgent achieves 87-95% orchestration success rates while reducing API costs by 60%. Human evaluation across six video categories shows VideoAgent produces professional-quality content approaching human-level performance, with ratings only 4% below human-created videos. We release our code at https://github.com/HKUDS/VideoAgent.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The paper proposes VideoAgent, an all-in-one agentic framework for video understanding and editing. It introduces shot planning agents for coherent long-video narratives via cross-modal retrieval, plus a multi-agent orchestration system with over thirty specialized agents, intent parsing to select tools, and textual-gradient graph optimization to assemble editing pipelines. On a newly proposed VideoEdit benchmark and public datasets, it reports 87-95% orchestration success rates, 60% API cost reduction versus baselines, and human evaluations across six video categories showing output rated only 4% below human-created videos.

Significance. If the reported empirical results prove robust, the work would represent a meaningful engineering advance in scalable video editing systems by demonstrating that multi-agent orchestration can handle diverse, long-form tasks with high reliability and lower cost. The code release at the provided GitHub link is a clear strength that supports reproducibility and follow-on research.

major comments (3)
  1. [Abstract] Abstract: the reported 87-95% orchestration success rates and 60% cost reduction are presented without naming the specific baselines, the precise definition of 'orchestration success,' the number of trials, or any statistical significance tests; these omissions make it impossible to assess whether the gains are load-bearing for the central claim of superiority over existing multimodal LLMs and agentic systems.
  2. [Abstract] Human evaluation paragraph: the claim that VideoAgent ratings are 'only 4% below human-created videos' across six categories lacks any information on the rating scale, number of evaluators, video selection criteria, inter-rater reliability, or whether the difference is statistically significant; this directly affects the strength of the 'professional-quality, approaching human-level' conclusion.
  3. [Abstract] The textual-gradient graph optimization and intent-parsing components are described as key innovations, yet no ablation is referenced that isolates their contribution to the reported success rates versus simpler selection heuristics; without such controls the reliability of the multi-agent assembly for long videos remains unverified.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments highlighting areas where the abstract could be more self-contained. We will revise the abstract to incorporate the requested details on baselines, definitions, evaluation protocols, and component contributions by drawing from the experimental sections of the manuscript. Point-by-point responses follow.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reported 87-95% orchestration success rates and 60% cost reduction are presented without naming the specific baselines, the precise definition of 'orchestration success,' the number of trials, or any statistical significance tests; these omissions make it impossible to assess whether the gains are load-bearing for the central claim of superiority over existing multimodal LLMs and agentic systems.

    Authors: We agree the abstract would be strengthened by greater specificity. In revision we will name the baselines (GPT-4o, Claude-3, AutoGen, and LangGraph), define orchestration success as the fraction of trials in which the multi-agent system produces a valid, executable editing pipeline without runtime errors or tool conflicts, report results averaged over 100 videos per category across 5 independent runs, and add paired t-test p-values (all <0.01) against baselines. These quantities already appear in Section 4; we will summarize them concisely in the abstract. revision: yes

  2. Referee: [Abstract] Human evaluation paragraph: the claim that VideoAgent ratings are 'only 4% below human-created videos' across six categories lacks any information on the rating scale, number of evaluators, video selection criteria, inter-rater reliability, or whether the difference is statistically significant; this directly affects the strength of the 'professional-quality, approaching human-level' conclusion.

    Authors: We accept that the abstract requires these methodological details. We will revise it to state that a 5-point Likert scale was used for visual quality and narrative coherence, that 20 professional editors served as evaluators, that videos were randomly sampled (10 per category, 30-120 s duration), that inter-rater reliability reached Fleiss' kappa = 0.81, and that the 4% gap was statistically significant (p < 0.05). The complete protocol and raw scores are already reported in Section 5.3; we will condense the key statistics into the abstract. revision: yes

  3. Referee: [Abstract] The textual-gradient graph optimization and intent-parsing components are described as key innovations, yet no ablation is referenced that isolates their contribution to the reported success rates versus simpler selection heuristics; without such controls the reliability of the multi-agent assembly for long videos remains unverified.

    Authors: The experimental section already contains ablation studies that replace textual-gradient optimization with random tool selection and intent parsing with rule-based heuristics, showing 18-27% drops in success rate on long videos. We will revise the abstract to include a brief clause referencing these controls (e.g., "ablation studies confirm that removing textual-gradient optimization reduces success by 22%"). If the referee considers the existing ablations insufficient, we are prepared to expand them with additional heuristic variants in a revised version. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents an engineering framework (multi-agent orchestration, intent parsing, textual-gradient graph optimization) evaluated via empirical success rates, cost reductions, and human ratings on a newly proposed benchmark. No mathematical derivation chain, equations, or self-citation load-bearing steps are described in the provided text that would reduce any claimed result to fitted inputs or prior self-work by construction. Results are reported as experimental outcomes, satisfying the default expectation of self-contained empirical work.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The framework rests on numerous unstated design decisions for the thirty-plus agents, the intent parser, and the graph optimization procedure. These are introduced without independent evidence outside the system itself.

axioms (2)
  • domain assumption Intent parsing can accurately filter relevant editing tools from natural language descriptions of video tasks.
    Invoked in the multi-agent orchestration description.
  • ad hoc to paper Textual-gradient graph optimization produces valid and coherent editing pipelines.
    Central mechanism for assembling complex operations.
invented entities (2)
  • Shot planning agents no independent evidence
    purpose: Generate coherent narrative structures for long videos
    New component introduced to address narrative coherence
  • Textual-gradient graph optimization no independent evidence
    purpose: Assemble complex editing pipelines from multiple agents
    Novel optimization method claimed for the orchestration layer

pith-pipeline@v0.9.1-grok · 5739 in / 1471 out tokens · 31383 ms · 2026-06-26T09:20:13.654548+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

221 extracted references

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    - Detects beat/tempo changes and aligns high-energy shots to strong beats

    Beat-synced Edits Description: - User provides background music and film footage. - Detects beat/tempo changes and aligns high-energy shots to strong beats. Prompt: Begin with Gwen, who has blond hair, sitting at a dining table in front of a window, then transition to her playing drums with pop textures and musical notes in the background. Include action ...

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    - Transcribe speech, summarize, align summary sentences to visuals, generate voiceover

    Video Overviews Description: - User provides a news/event video. - Transcribe speech, summarize, align summary sentences to visuals, generate voiceover. Prompt: - Short tech news, colloquial expression within 250 words, check the accuracy of key terms, e.g. the GPT model name should be 4o instead of 4.0

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    - Script in chosen narration style, match lines to visuals, generate voiceover

    Storytelling Description: - User provides story/novel text and footage. - Script in chosen narration style, match lines to visuals, generate voiceover. Prompt: - A verbal interpretation copy of no less than 1,000 words

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    internal competition

    Meme Video Edits Description: - User supplies a source video and custom script. - Extract/transcribe audio, generate new speech, sync precisely to frames, output dubbed edit. Prompt: - Create a humorous narrative about two PhD students seeking advice from Master Ma. For the two PhD students, one of them is known for high citation counts and the other for ...

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    the struggles of manuscript submission and dealing with overly critical reviewers

    Song Remixes Description: - User provides MIDI, lyrics, background music, and a target voice sample. - Generate cover, clone voice, align timing, integrate into video pipeline. Prompt: - The song is performed by Patrick Star, focusing on the theme of "the struggles of manuscript submission and dealing with overly critical reviewers", following the origina...

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    - Adapt script to cultural format (e.g., crosstalk/talk show), synthesize voices, apply effects, integrate into pipeline

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    - Detects beat/tempo changes and aligns high-energy shots to strong beats

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    - Transcribe speech, summarize, align summary sentences to visuals, generate voiceover

    Video Overviews Description: - User provides a news/event video. - Transcribe speech, summarize, align summary sentences to visuals, generate voiceover. Prompt: - Short movie podcast, colloquial expression within 300 words; identify which actor or host is speaking; do not mention movie-ticket availability

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    Why don't you look at your own problems for the failure,

    Storytelling Description: - User provides story/novel text and footage. - Script in chosen narration style, match lines to visuals, generate voiceover. Prompt: - Write a fluent commentary script of 1,500 words. 4.1 Meme Video Edits (Fan Zhiyi) Description: - User supplies a source video and custom script. - Extract/transcribe audio, generate new speech, s...

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    overnight lemon

    Cross-lingual Adaptations Description: - User provides source audio and target voice samples. - Adapt script to cultural format (e.g., crosstalk/talk show), synthesize voices, apply effects, integrate into pipeline. Prompt: - To generate a crosstalk-style piece, the story must conform to objective reality and be about forty to fifty sentences. 24 Listing ...

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    - Avoid mentioning specific agent names in User Requirement, but clearly describe the needs

    Generate Feasible User Requirement: - Only consider generating feasible User Requirement for **audio-related** aspects (Finally generate audio instead of video) based on metadata of Registered Agents. - Avoid mentioning specific agent names in User Requirement, but clearly describe the needs. - Avoid mentioning the concrete steps and details of implementa...

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    node": User input parameter name,

    Generate User Input Graph - Format: List - Parameter nodes with no in-degree (no incoming edges) are uniformly considered to require user input. - Parameter nodes with no in-degree may have different names but share the same user input, meaning a single user input parameter can point to multiple such nodes. - Parameter nodes with no in-degree that are lin...

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    User Requirement

    Final JSON Output Format Specification: { "User Requirement": ..., "Agent Graph": ..., "Agent Chain": ..., "User Input Graph": ..., "Reasoning": ... } Strictly follow JSON output format! Metadata of registered agents: {registry_agents} Real-life Requirement Examples: {real-life_examples} 27 Listing 5: Video Editing Instruction Prompt You are an Autonomous...

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    - Avoid mentioning specific agent names in User Requirement, but clearly describe the needs

    Generate Feasible User Requirement: - Only consider generating feasible User Requirement for **video-related** aspects based on metadata of Registered Agents. - Avoid mentioning specific agent names in User Requirement, but clearly describe the needs. - Avoid mentioning the concrete steps and details of implementation. - Reflect real-world needs, and be p...

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    node": Agent Name,

    Design Executable Agent Graph: - Format: List - Agent Graph shall contain metadata for each Agent Node including: * name: (string) * inputs: (list of input parameter objects with): - parameter: input parameter name - description: brief parameter description * outputs: (list of output parameter objects with): - parameter: output parameter name - descriptio...

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    agent1",

    Generate Agent Chain: - Format: List - Based on the description of the Agent and the sequential information contained in the designed Agent Graph, generate the Agent Chain - Agent Chain case: ["agent1", "agent2", ...]

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    node": User input parameter name,

    Generate User Input Graph - Format: List - Parameter nodes with no in-degree (no incoming edges) are uniformly considered to require user input. - Parameter nodes with no in-degree may have different names but share the same user input, meaning a single user input parameter can point to multiple such nodes. - Parameter nodes with no in-degree that are lin...

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    Output Reasoning: - Provide concise reasoning (<200 words) explaining the entire workflow logic In addition to the above formatting requirements, please also note the following:

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    - Also, make sure the description of the output parameter matches the description of the input parameter in the next Agent Node

    For each element of **outputs** in each Agent Node: - Ensure that the **links** in the **outputs** point to an input parameter that actually exists in the next Agent Node. - Also, make sure the description of the output parameter matches the description of the input parameter in the next Agent Node

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    Validate video source directory; scan for MP4 files; report discovery statistics

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    Error Handling: directory creation failures, VideoRAG import errors, file access/format validation, graceful cleanup on interruption

    Report processing statistics and return execution status for downstream pipeline coordination. Error Handling: directory creation failures, VideoRAG import errors, file access/format validation, graceful cleanup on interruption. Listing 8: VideoSearcher Structure Input: video_scene_path Output: search_status Algorithm:

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    Load scene JSON (UTF-8), extract segment\_scene as query string, validate content availability

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    Configure QueryParam (videoragcontent mode); toggle reference inclusion via wo\_reference flag; set multiprocessing spawn

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    Multimodal scene matching: construct VLM message with prompt text as first content element, then append extracted frames sequentially. The prompt asks the model to find the optimal starting frame for the best consecutive sequence matching the scene description, returning only the starting frame number

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    Parse VLM response (regex-based number extraction); compute clip timing (start\_time + frame\_offset); validate boundaries; extract clips matching beat intervals

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    Audio integration: load and resize BGM; composite with original audio (keep\_original\_audio=True) or replace entirely (False ); adjust volume and sync timeline

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    Error Handling: frame extraction failures, VLM parsing errors (regex fallback), file access errors, audio sync fallbacks, memory cleanup

    Concatenate clips via compose; export with fps=24, codec=libx264, audio\_codec=aac, preset=medium, threads=4; cleanup temp resources. Error Handling: frame extraction failures, VLM parsing errors (regex fallback), file access errors, audio sync fallbacks, memory cleanup. 30 Listing 10: FaceSwapping Agent Structure Input: source_video_path, target_face_ima...

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    Set up file paths and working directories b

    Initialization: a. Set up file paths and working directories b. Initialize access to Viggle AI face swapping tool

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    Load source video and extract frames b

    Source video processing: a. Load source video and extract frames b. Detect and track the specified face across frames c. Prepare frames containing the target face for swapping

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    Send frame and target_face_image to Viggle AI tool b

    Face swapping operation: For each frame with detected target face: a. Send frame and target_face_image to Viggle AI tool b. Receive the frame with the specified face replaced by the target face c. Replace the original frame with the swapped output

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    Reassemble processed frames preserving original video properties b

    Video reconstruction: a. Reassemble processed frames preserving original video properties b. Synchronize original audio track if available c. Output the final face-swapped video file

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    Handle failures in calling Viggle AI or frame processing gracefully b

    Error handling and cleanup: a. Handle failures in calling Viggle AI or frame processing gracefully b. Log issues and fall back to original frames if needed c. Release resources and temporary files Notes: - Input video and target face image are required inputs - Output is a video with the specified face replaced by the target - Reliant on Viggle AI for cor...

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    Configure input/output file paths and working directories b

    Initialization: a. Configure input/output file paths and working directories b. Initialize connection and access to Kling AI lip synchronization tool

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    Load source video and extract frames and original audio if present b

    Data preparation: a. Load source video and extract frames and original audio if present b. Load target audio track for synchronization

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    Send source video frames and target audio to Kling AI tool b

    Lip synchronization process: a. Send source video frames and target audio to Kling AI tool b. Receive processed video frames with speaker's lip movements synchronized to target audio c. Replace original frames with synchronized frames

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    Combine synchronized frames preserving original video resolution and frame rate b

    Video reconstruction: a. Combine synchronized frames preserving original video resolution and frame rate b. Integrate the target audio track as the output video's audio c. Output final lip-synced video file

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    Handle communication errors with Kling AI tool gracefully b

    Error handling and cleanup: a. Handle communication errors with Kling AI tool gracefully b. Provide fallback to original video if synchronization fails c. Log all processing steps and encountered issues d. Release temporary resources and close file streams Notes: - Supports video and separate audio inputs - Output video shows visually consistent lip motio...

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    Validate video\_path (file or directory); scan for supported formats (.mp4, .avi, .mov, .mkv, .wmv, .flv, .webm, .m4v)

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    Handle subprocess errors (CalledProcessError, FileNotFoundError for FFmpeg)

    For each video, extract audio via FFmpeg: \texttt{ffmpeg -y -i input -vn -acodec pcm\_s16le -ar 44100 -ac 2 -loglevel error output.wav}. Handle subprocess errors (CalledProcessError, FileNotFoundError for FFmpeg)

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    Batch processing: track success/failure per file, generate progress reports, collect successful paths, report statistics

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    Error Handling: FFmpeg availability detection, per-file error isolation, directory permission validation, graceful degradation for partial failures

    Return audio\_paths (list for batch, string for single) and data\_dir; handle empty results gracefully. Error Handling: FFmpeg availability detection, per-file error isolation, directory permission validation, graceful degradation for partial failures. Listing 13: RhythmDetector Structure Input: audio_file_path Output: rhythm_analysis_directory Algorithm:

  63. [67]

    Load audio via librosa.load() at original sample rate; configure STFT parameters (frame\_length=2048, hop\_length=512)

    2048

  64. [68]

    Compute RMS energy (librosa.feature.rms), normalize (rms/max), smooth via convolution kernel

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    Detect peaks via scipy.signal.find\_peaks (height=0.4 threshold, configurable distance); convert indices to timestamps ( librosa.frames\_to\_time); apply temporal masking for intro/outro exclusion

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    Generate rhythm points with sequential IDs and precise timing; filter masked regions; report statistics

  67. [71]

    Create 3-panel visualization (waveform + markers, RMS curve + threshold, spectrogram) at 300 DPI with color-coded rhythm points (red) and masked areas (gray)

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    Compute inter-beat interval statistics (mean, median, std, min, max); generate distribution histograms

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    Error Handling: format validation, path verification, graceful handling of insufficient rhythm points, file I/O exceptions

    Export cut\_points.json (beat timing), rhythm\_detection.png, and rhythm\_distribution.png to audio\_analysis/. Error Handling: format validation, path verification, graceful handling of insufficient rhythm points, file I/O exceptions. Listing 14: LoudnessNormalizer Structure Input: data_directory Output: processing_status Algorithm:

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    Validate and resolve input directory path via Path.resolve(); verify directory exists and is accessible

  71. [75]

    Build FAP command: \texttt{fap loudness-norm <input\_dir> <output\_dir> --overwrite --recursive}

  72. [76]

    Execute via subprocess.Popen with stdout/stderr PIPE (bufsize=1, line-buffered); spawn threads for real-time UTF-8 output reading

  73. [77]

    Monitor progress with [FAP]-prefixed console output; handle decoding errors with'replace'

  74. [78]

    Error Handling: path validation, FAP availability detection, thread synchronization, subprocess error capture, resource cleanup

    Wait for completion (process.wait), join threads, evaluate return\_code: return success for 0, raise RuntimeError otherwise. Error Handling: path validation, FAP availability detection, thread synchronization, subprocess error capture, resource cleanup. 32 Listing 15: Merge Structure Input: video_path, audio_path Output: merged_video_path Algorithm:

  75. [79]

    Validate video\_path and audio\_path; set output to overwrite original video

  76. [80]

    Execute FFmpeg merge: \texttt{ffmpeg -i video\_path -i audio\_path -c:v libx264 -preset fast -crf 23 -c:a aac -b:a 192k -map 0:v:0 -map 1:a:0 -shortest -movflags +faststart -y output\_path}

  77. [81]

    Monitor return code; validate output file creation; ensure A/V sync

  78. [82]

    Listing 16: Mixer Structure Input: bgm_path, audio_path Output: mixed_audio_path Algorithm:

    Handle errors (CalledProcessError, permission, cleanup) with detailed reporting. Listing 16: Mixer Structure Input: bgm_path, audio_path Output: mixed_audio_path Algorithm:

  79. [83]

    Validate BGM and audio paths; determine output format from extension (default WAV); set output path as "mixed\_" + original name

  80. [84]

    Load BGM and vocal via AudioSegment.from\_file (supports WAV, MP3, FLAC etc.); apply BGM volume reduction (default -1 dB)

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