According to the Runway ML 2024 technical white paper, broad image to video ai tools such as the Gen-2 model can receive PNG/JPG input up to 8K resolution (7680×4320 pixels), and the maximum size of the single file is increased from 50MB of general AI tools to 2GB. But in case of making 4K video, the use rate of video memory is 32GB (the highest for NVIDIA RTX 6000 Ada graphics is 48GB). For example, Disney animation studio uses this tool to run images of 8192×4320 pixel RAW format conversion. The duration to produce a 5-second video has been cut from 6 hours on a standard workstation to 22 minutes, but with a cloud computing fee of $0.28 per second (the scenario is quoted from the SIGGRAPH 2024 presentation). Nevertheless, SONY testing demonstrated that if the input image was over 60 million pixels, the color depth deviation ΔE value of the ai video generator increased from an average of 2.3 to 5.7 (industry standard ΔE≤3), and the rate of skin tone distortion increased by 18% (data source: CineGear 2024 Technical Report).
Technical limitations manifest significantly at extreme resolutions. The MIT Media Lab’s 2024 test proves that during rendering of 8K PNGs with 16-bit color depth (approximately 1.2GB per image), the frame rendering failure rate of image to video ai is up to 15%. The reason is mainly that the video memory bandwidth is over 600GB/s (NVIDIA H100’s maximum is 900GB/s). Though Adobe Firefly Video model supports input of 6144×3160 pixels, in the course of generating a 1080p video, the error rate of Alpha channel transparency is at most 12%, while through manual synthesis via expert software After Effects, this error rate remains at 0.8%. For instance, when the movie “Alien: Awakening” was being filmed, 23% of frames of the AI-generated 8K biological skin textures had normal map misalignment that required frame-by-frame manual fix (quoted figures from Variety’s June 2024 report).
Industry solutions are evolving extremely rapidly. The VEAI 4.0 video creator developed by NVIDIA with the cooperation of Topaz Labs has accelerated image generation at 16K resolution (15360×8640 pixels) to 3.2 seconds per frame utilizing block processing technology (while taking 14 seconds per frame when done the old way), but equipment costs have reached astronomical figures: The powerful workstation with 4 RTX 6000 Ada graphics cards costs up to 84,000 US dollars. In its business case, Netflix utilized the technology to batch render 4K stills collections. The total rendering time for 100,000 images to video was reduced from 42 days of traditional rendering to 9 days, but an additional 75,000 US dollar cloud surcharge was incurred (the case was for the 2024 NAB Show Summit). On the smartphone side, iOS app of Luma AI supports instant processing of 48-megapixel ProRAW photos with a file size of about 75MB, and the endpoint power consumption during the generation of 1080p video is regulated at 5.2W (battery capacity of iPhone 15 Pro Max is 4422mAh), but there is just a 4:2:0 color sampling. It discards 37% chromatic information as compared to the 4:4:4 of pros’ cameras.
File compatibility differences affect market choices. Tests in 2024 showed Stable Video Diffusion had an implementation rate of only 68% for PNG transparency channels, and JPG files lost 14% of the dynamic range of the output video because of lossy compression (peak brightness dropped from 1000 nits to 850 nits). In television and cinema industrialized settings, Industrial Light and Magic (ILM) requires image to video ai to possess EXIF metadata parsing. However, in its current situation, only 35% of the tools actually read the ICC profile of the medium format cameras such as 150MP Phase One IQ4 image. It is interesting to see that Google’s newly introduced Lumiere Pro model has set the support level of entering 12,000 ×6300 pixels. The PSNR (Peak Signal-to-Noise Ratio) for generating 4K videos is as much as 42dB, which is 13% higher than industry standards. Nevertheless, the per-minute price of generation remains as high as $4.7 (the price of the traditional process is $0.6 per minute). As the cost of computing power decreases, 78% of ai video generator may natively support resolutions over 10K in 2025. Yet color accuracy and physical accuracy are still the significant points of innovation (the forecasted data relies on IDC’s 2025 AI Vision report).