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BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Implementation pattern included in full analysis above.

MVP Investment

$9K - $12K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

S

Sayan Deb Sarkar

Stanford University

R

Rémi Pautrat

Microsoft Spatial AI Lab

O

Ondrej Miksik

Microsoft Spatial AI Lab

M

Marc Pollefeys

ETH Zurich

Find Similar Experts

Video experts on LinkedIn & GitHub

Founder's Pitch

"CoPE-VideoLM drastically improves video processing efficiency by using codec primitives for lightweight video tokenization in AI models."

Video Processing and AnalysisScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

3/4 signals

7.5

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

Why It Matters

This research is significant as it addresses the inefficiencies in current Video Language Models by leveraging inherent video data properties, thus reducing computational costs and speeds up real-time video understanding applications.

Product Angle

Productizing this involves creating an API that integrates with existing video processing software to optimize video frame analysis and storage.

Disruption

This approach can potentially replace traditional video processing techniques that require dense frame processing, offering a much more efficient solution without loss in fidelity.

Product Opportunity

The market opportunity is substantial, especially in sectors relying heavily on video data, such as security, entertainment, and remote communications, where reducing processing costs and improving efficiency is critical.

Use Case Idea

A commercial application could be a real-time video processing tool for video conferencing platforms, reducing data usage while preserving video quality.

Science

The paper outlines a method where video codec primitives, such as motion vectors and residuals, are used to represent video frames in a sparser format, reducing the need for dense image conversion and cutting computational overhead.

Method & Eval

The method leverages codec primitives and employs lightweight transformer-based encoders, validated through reduction in token usage and improved performance on various video understanding benchmarks compared to traditional models.

Caveats

Potential limitations could include reliance on the codec data quality and possible integration challenges with existing systems which don't use standardized coding methods.

Author Intelligence

Sayan Deb Sarkar

Stanford University
sdsarkar@stanford.edu

Rémi Pautrat

Microsoft Spatial AI Lab

Ondrej Miksik

Microsoft Spatial AI Lab

Marc Pollefeys

ETH Zurich

Iro Armeni

Stanford University

Mahdi Rad

Microsoft Spatial AI Lab

Mihai Dusmanu

Microsoft Spatial AI Lab

References (100)

[1]
Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications
2025Kento Kawaharazuka, Jihoon Oh et al.
[2]
Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding
2025Yuanhan Zhang, Yunice Chew et al.
[3]
EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
2025Ruihan Yang, Qinxi Yu et al.
[4]
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
2025Gheorghe Comanici, Eric Bieber et al.
[5]
TriVLA: A Triple-System-Based Unified Vision-Language-Action Model with Episodic World Modeling for General Robot Control
2025Zhenyang Liu, Yongchong Gu et al.
[6]
LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs
2025Boyuan Sun, Jiaxin Zhao et al.
[7]
Flexible Frame Selection for Efficient Video Reasoning
2025S. Buch, Arsha Nagrani et al.
[8]
FlexSelect: Flexible Token Selection for Efficient Long Video Understanding
2025Yunzhu Zhang, Yu Lu et al.
[9]
Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness
2025Haochen Wang, Yucheng Zhao et al.
[10]
Towards Scalable Modeling of Compressed Videos for Efficient Action Recognition
2025Shristi Das Biswas, Efstathia Soufleri et al.
[11]
Efficient Motion-Aware Video MLLM
2025Zijia Zhao, Yuqi Huo et al.
[12]
AdaReTaKe: Adaptive Redundancy Reduction to Perceive Longer for Video-language Understanding
2025Xiao Wang, Qingyi Si et al.
[13]
Qwen2.5-VL Technical Report
2025Shuai Bai, Keqin Chen et al.
[14]
Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos
2025Kairui Hu, Penghao Wu et al.
[15]
VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
2025Boqiang Zhang, Kehan Li et al.
[16]
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
2025Shaolei Zhang, Qingkai Fang et al.
[17]
VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling
2024Xinhao Li, Yi Wang et al.
[18]
FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Visual Language Models
2024Tianyu Fu, Tengxuan Liu et al.
[19]
FastVLM: Efficient Vision Encoding for Vision Language Models
2024Pavan Kumar Anasosalu Vasu, Fartash Faghri et al.
[20]
Apollo: An Exploration of Video Understanding in Large Multimodal Models
2024Orr Zohar, Xiaohan Wang et al.

Showing 20 of 100 references