Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63298
Volume:
10 |
5 |
May 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Engineering
Late G.N. Sapkal College of Engineering, Nashik
Department of Computer Engineering
Late G.N. Sapkal College of Engineering, Nashik
Department of Computer Engineering
Late G.N. Sapkal College of Engineering, Nashik
Department of Computer Engineering
Late G.N. Sapkal College of Engineering, Nashik
Department of Computer Engineering
Late G.N. Sapkal College of Engineering, Nashik
Today, in the online fashion world, brands need eye- catching videos to attract customers. But making these videos is not easy. It usually involves hiring models, doing photoshoots, and editing videos, which takes a lot of time and money. Because of this, many small clothing businesses are not able to create good advertisements regularly. AdCraftor is made to solve this problem in a simple way. User uploads a product image and adds a few details. The system automatically creates a model image, selects the best one, and makes a short ad video. User just waits and gets the final video. Adcraftor is mainly suitable for small businesses because it make advertisement fast , efficient and affordable.
KEYWORDS
The system Adcraftor shows using AI and cloud technology it is easier to create advertisement video without any manual work Instead of doing everything manually, the system handles the full process. It takes the product image, understands it, and then creates the final advertisement video. This saves both time and money, while still giving good results. This system is very useful for small and medium businesses. small business which having low budget they also promotes their products using this system. Also, the system is flexible, so it can be improved easily in the future when better AI tools are available.
1) M. Wei, C. Yu, J. Zhou, and F. Wang, “3DV-TON: Textured 3D Guided Consistent Video Try-on via Diffusion Models,” Apr. 2025. 2) D. Qian, K. Su, Y. Tan, et al., “VC-LLM: Automated Advertisement Video Creation from Raw Footage using Multimodal LLMs,” Apr. 2025. 3) J. Wang, W. Sun, M. Li, Y. Zheng, F. Li, Z. Tao, D. Di, H. Li, W. Chen, and X. Huang, “Chrono Tailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On,” Jun. 2025. 4) A. Smith and B. Jordan, “Video Diffusion Generation: Com prehensive Review and Open Problems,” Aug. 2025. 5) A. Melnik, M. Ljubljanac, C. Lu, Q. Yan, W. Ren, and H. Ritter, “Video Diffusion Models: A Survey,” May 2024. 6) X. Chen, Y. Zhang, and H. Wu, “A Markov Chain Approach for Video-Based Virtual Try-On with Denoising Diffusion GAN,” Sept. 2024. 7) A. Gupta, L. Yu, K. Sohn, X. Gu, M. Hahn, F.-F. Li, and I. Essa, “Photorealistic Video Generation with Diffusion Models,” Nov. 2024. 8) H. Nguyen, Q. N. Q. Nguyen, K. Nguyen, and R. Nguyen, “SwiftTry: Fast and Consistent Video Virtual Try-On with Diffusion Models,” Dec. 2024. 9) J. Karras, Y. Li, N. Liu, L. Zhu, I. Yoo, A. Lugmayr, C. Lee, and I. Kemelmacher-Shlizerman, “Fashion-VDM: Video Diffusion Model for Virtual Try-On,” Dec. 2024. 10) J. Zeng, D. Song, W. Nie, H. Tian, T. Wang, and A. Liu, “CAT DM: Controllable Accelerated Virtual Try-on with Diffusion Model,” Nov. 2023
Publication Date
5/19/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...