Generative video systems, also known as generative adversarial networks (GANs), have made significant advancements in recent years in generating realistic images, videos, and even music. However, despite these advancements, they still struggle to create complete movies that are coherent and engaging from start to finish.
One of the main challenges that generative video systems face is the complexity and long-form nature of movies. Movies typically consist of multiple scenes, characters, dialogue, and plotlines that need to be cohesive and engaging for viewers. Generative video systems often struggle to generate content that is consistent and logical throughout an entire movie.
Another challenge is the lack of understanding of narrative structure and storytelling principles by these systems. Movies rely heavily on storytelling techniques such as character development, plot progression, and emotional arcs to keep viewers engaged. Generative video systems may be able to generate visually impressive scenes, but they often lack the ability to create a compelling narrative that ties everything together.
Furthermore, generative video systems also struggle with producing content that is original and innovative. Movies often require creativity and originality to stand out in a crowded marketplace. Generative video systems may be able to generate content that is visually appealing, but they often lack the ability to create something truly unique and groundbreaking.
In conclusion, while generative video systems have made significant advancements in generating realistic content, they still face significant challenges when it comes to creating complete movies. The complexity of movies, the lack of understanding of narrative structure, and the difficulty in producing original content are all obstacles that these systems need to overcome in order to create complete and compelling movies in the future.
Generative AI video systems have made significant advancements in recent years, leading many to speculate about the possibility of individuals creating Hollywood-style blockbusters from the comfort of their own homes. However, there are several fundamental reasons why generative video systems based on Latent Diffusion Models are not yet capable of producing complete movies.
One of the biggest challenges is narrative inconsistency. Current video generation systems rely on denoising diffusion models that utilize random noise, making it difficult to create accurate ‘follow on’ shots that maintain consistency with previous scenes. While text prompts and seed images can elicit semantically-appropriate content from the model’s latent space, the random noise factor results in variations each time a shot is generated. This leads to shifting identities of characters and inconsistent environments, making it challenging to create a cohesive narrative.
Systems that produce still images, such as NVIDIA’s ConsiStory and projects like TheaterGen and DreamStory, offer narrative consistency but are limited to static visuals. In order to create a sequence of video shots with consistent characters and environments, additional complexity is introduced, such as training Low Rank Adaptation (LoRA) models for each element in the scene. This process can be time-consuming and requires multiple trained models, making it impractical for creating full-length movies.
While video-to-video conversion can offer alternative interpretations of footage through text prompts, the current state of generative AI technology still falls short of producing complete, Hollywood-level movies with consistent characters, narrative continuity, and total photorealism. As technology continues to advance, it is possible that individuals may eventually be able to create movies on par with professional productions, but for now, there are significant roadblocks preventing generative video systems from making complete movies.
Generative video systems have made significant advancements in recent years, offering the potential to create realistic and compelling video content using artificial intelligence. However, despite these advances, there are still significant challenges that prevent these systems from being able to create complete movies seamlessly.
One major issue is the need to create the core footage for the movie before using generative video systems. This essentially means that filmmakers are required to create the movie twice – once in the form of CGI models and then again using the generative system. Even with synthetic systems like Unreal’s MetaHuman, the consistency across shots cannot be guaranteed when using CGI models in a video-to-image transformation.
Moreover, diffusion-based video models lack the ability to see the ‘big picture’ and have a limited memory of past frames, which makes it challenging for them to maintain a consistent appearance across shots. This limitation becomes apparent when trying to edit a single aspect of a generated video shot, as changing one aspect can lead to multiple other aspects being altered as well.
Another significant challenge is the inability of generative video systems to rely on the laws of physics. Traditional CGI methods offer algorithmic physics-based models that can simulate various real-world phenomena, but diffusion-based methods struggle to accurately depict complex scenes and specific instances of cause and effect.
Furthermore, generative video systems face difficulties in depicting rapid movements, obtaining temporal consistency in output video, and creating specific facial performances such as lip-sync for dialogue. While ancillary systems like LivePortrait and AnimateDiff are being used to address these issues, they do not fully solve the underlying limitations of generative video systems.
In conclusion, the current limitations of generative video systems make it challenging for them to create coherent and photorealistic blockbuster-style full-length movies with realistic dialogue, performances, environments, and continuity. While there is ongoing research to address these limitations, it is clear that significant hurdles remain before generative video systems can effectively produce complete movies on their own. Generative Video Systems dan Tantangannya dalam Membuat Film Lengkap
Teknologi generative video saat ini sering muncul sebagai komponen tambahan dalam arsitektur alternatif. Meskipun studio film mungkin berharap bahwa pelatihan pada katalog film yang sah dapat menghilangkan seniman efek visual (VFX), kecerdasan buatan (AI) sebenarnya menambahkan peran baru ke dalam angkatan kerja saat ini.
Apakah sistem video berbasis difusi benar-benar dapat diubah menjadi pembuat film yang konsisten secara naratif dan fotorealistik, atau apakah seluruh bisnis ini hanyalah pengejaran alkimia, akan menjadi jelas dalam 12 bulan ke depan. Mungkin kita memerlukan pendekatan yang sama sekali baru; atau mungkin Gaussian Splatting (GSplat), yang dikembangkan pada awal tahun 1990-an dan baru-baru ini menjadi populer dalam ruang sintesis gambar, merupakan alternatif potensial untuk generasi video berbasis difusi.
Karena GSplat membutuhkan waktu 34 tahun untuk muncul ke permukaan, mungkin juga pesaing lama seperti NeRF dan GANs – dan bahkan model difusi laten – masih harus menunggu saatnya. Meskipun fitur AI Storyboard dari Kaiber menawarkan fungsionalitas semacam ini, hasil yang saya lihat tidak memiliki kualitas produksi.
Martin Anderson adalah mantan kepala konten penelitian ilmiah di metaphysic.ai. Pertama kali diterbitkan pada hari Senin, 23 September 2024.
Tantangan utama yang dihadapi oleh generative video systems dalam membuat film lengkap adalah menciptakan konsistensi naratif dan kualitas fotorealistik yang sebanding dengan film yang dibuat oleh manusia. Meskipun teknologi AI terus berkembang pesat, masih ada beberapa hambatan yang harus diatasi sebelum generative video systems dapat menghasilkan film lengkap dengan kualitas produksi yang memadai.
Salah satu masalah utama adalah kemampuan generative video systems untuk menghasilkan cerita yang kohesif dan memikat. Meskipun teknologi AI dapat melibatkan berbagai data dan informasi untuk menciptakan konten visual, kemampuan untuk mengembangkan alur cerita yang kuat dan karakter yang kompleks masih merupakan tantangan yang belum terpecahkan.
Selain itu, masalah kualitas visual juga menjadi perhatian utama. Meskipun generative video systems dapat menghasilkan gambar dan efek visual yang menakjubkan, masih ada kekurangan dalam hal reproduksi warna, tekstur, dan detail yang diperlukan untuk menciptakan pengalaman visual yang memuaskan.
Dengan perkembangan teknologi yang terus berlanjut, diharapkan bahwa generative video systems akan terus meningkatkan kemampuannya dalam membuat film lengkap. Namun, hingga saat ini, masih diperlukan penelitian dan pengembangan lebih lanjut untuk mengatasi tantangan yang dihadapi oleh teknologi ini.