The form of content material that customers would possibly wish to create utilizing a generative mannequin similar to Flux or Hunyuan Video might not be at all times be simply out there, even when the content material request is pretty generic, and one would possibly guess that the generator might deal with it.
One instance, illustrated in a brand new paper that we’ll check out on this article, notes that the increasingly-eclipsed OpenAI Sora mannequin has some problem rendering an anatomically right firefly, utilizing the immediate ‘A firefly is glowing on a grass’s leaf on a serene summer season evening’:
OpenAI’s Sora has a barely wonky understanding of firefly anatomy. Supply: https://arxiv.org/pdf/2503.01739
Since I hardly ever take analysis claims at face worth, I examined the identical immediate on Sora at this time and bought a barely higher consequence. Nonetheless, Sora nonetheless didn’t render the glow appropriately – somewhat than illuminating the tip of the firefly’s tail, the place bioluminescence happens, it misplaced the glow close to the insect’s toes:
My very own check of the researchers’ immediate in Sora produces a consequence that reveals Sora doesn’t perceive the place a Firefly’s gentle really comes from.
Paradoxically, the Adobe Firefly generative diffusion engine, skilled on the corporate’s copyright-secured inventory images and movies, solely managed a 1-in-3 success price on this regard, once I tried the identical immediate in Photoshop’s generative AI characteristic:
Solely the ultimate of three proposed generations of the researchers’ immediate produces a glow in any respect in Adobe Firefly (March 2025), although at the very least the glow is located within the right a part of the insect’s anatomy.
This instance was highlighted by the researchers of the brand new paper for example that the distribution, emphasis and protection in coaching units used to tell well-liked basis fashions could not align with the person’s wants, even when the person just isn’t asking for something notably difficult – a subject that brings up the challenges concerned in adapting hyperscale coaching datasets to their best and performative outcomes as generative fashions.
The authors state:
‘[Sora] fails to seize the idea of a glowing firefly whereas efficiently producing grass and a summer season [night]. From the info perspective, we infer that is primarily as a result of [Sora] has not been skilled on firefly-related matters, whereas it has been skilled on grass and evening. Moreover, if [Sora had] seen the video proven in [above image], it’ll perceive what a glowing firefly ought to appear to be.’
They introduce a newly curated dataset and recommend that their methodology might be refined in future work to create information collections that higher align with person expectations than many present fashions.
Knowledge for the Individuals
Basically their proposal posits an information curation strategy that falls someplace between the customized information for a model-type similar to a LoRA (and this strategy is much too particular for common use); and the broad and comparatively indiscriminate high-volume collections (such because the LAION dataset powering Steady Diffusion) which aren’t particularly aligned with any end-use situation.
The brand new strategy, each as methodology and a novel dataset, is (somewhat tortuously) named Customers’ FOcus in text-to-video, or VideoUFO. The VideoUFO dataset includes 1.9 million video clips spanning 1291 user-focused matters. The matters themselves have been elaborately developed from an present video dataset, and parsed by means of numerous language fashions and Pure Language Processing (NLP) methods:
Samples of the distilled matters offered within the new paper.
The VideoUFO dataset encompasses a excessive quantity of novel movies trawled from YouTube – ‘novel’ within the sense that the movies in query don’t characteristic in video datasets which might be at the moment well-liked within the literature, and subsequently within the many subsets which were curated from them (and most of the movies have been actually uploaded subsequent to the creation of the older datasets thar the paper mentions).
In actual fact, the authors declare that there’s solely 0.29% overlap with present video datasets – a powerful demonstration of novelty.
One purpose for this may be that the authors would solely settle for YouTube movies with a Artistic Commons license that may be much less prone to hamstring customers additional down the road: it is doable that this class of movies has been much less prioritized in prior sweeps of YouTube and different high-volume platforms.
Secondly, the movies have been requested on the idea of pre-estimated user-need (see picture above), and never indiscriminately trawled. These two elements together might result in such a novel assortment. Moreover, the researchers checked the YouTube IDs of any contributing movies (i.e., movies that will later have been break up up and re-imagined for the VideoUFO assortment) in opposition to these featured in present collections, lending credence to the declare.
Although not the whole lot within the new paper is sort of as convincing, it is an attention-grabbing learn that emphasizes the extent to which we’re nonetheless somewhat on the mercy of uneven distributions in datasets, by way of the obstacles the analysis scene is usually confronted with in dataset curation.
The new work is titled VideoUFO: A Million-Scale Consumer-Targeted Dataset for Textual content-to-Video Technology, and comes from two researchers, respectively from the College of Know-how Sydney in Australia, and Zhejiang College in China.
Choose examples from the ultimate obtained dataset.
A ‘Private Shopper’ for AI Knowledge
The subject material and ideas featured within the complete sum of web pictures and movies don’t essentially replicate what the typical finish person could find yourself asking for from a generative system; even the place content material and demand do are inclined to collide (as with porn, which is plentifully out there on the web and of nice curiosity to many gen AI customers), this may increasingly not align with the builders’ intent and requirements for a brand new generative system.
Moreover the excessive quantity of NSFW materials uploaded each day, a disproportionate quantity of net-available materials is prone to be from advertisers and people making an attempt to govern website positioning. Business self-interest of this type makes the distribution of subject material removed from neutral; worse, it’s tough to develop AI-based filtering techniques that may deal with the issue, since algorithms and fashions developed from significant hyperscale information could in themselves replicate the supply information’s tendencies and priorities.
Due to this fact the authors of the brand new work have approached the issue by reversing the proposition, by means of figuring out what customers are prone to need, and acquiring movies that align with these wants.
On the floor, this strategy appears simply as prone to set off a semantic race to the underside as to attain a balanced, Wikipedia-style neutrality. Calibrating information curation round person demand dangers amplifying the preferences of the lowest-common-denominator whereas marginalizing area of interest customers, since majority pursuits will inevitably carry higher weight.
Nonetheless, let’s check out how the paper tackles the problem.
Distilling Ideas with Discretion
The researchers used the 2024 VidProM dataset because the supply for subject evaluation that may later inform the undertaking’s web-scraping.
This dataset was chosen, the authors state, as a result of it’s the solely publicly-available 1m+ dataset ‘written by actual customers’ – and it needs to be said that this dataset was itself curated by the 2 authors of the brand new paper.
The paper explains*:
‘First, we embed all 1.67 million prompts from VidProM into 384-dimensional vectors utilizing SentenceTransformers Subsequent, we cluster these vectors with Ok-means. Word that right here we preset the variety of clusters to a comparatively massive worth, i.e., 2, 000, and merge comparable clusters within the subsequent step.
‘Lastly, for every cluster, we ask GPT-4o to conclude a subject [one or two words].’
The authors level out that sure ideas are distinct however notably adjoining, similar to church and cathedral. Too granular a standards for instances of this type would result in idea embeddings (as an illustration) for every kind of canine breed, as an alternative of the time period canine; whereas too broad a standards might corral an extreme variety of sub-concepts right into a single over-crowded idea; subsequently the paper notes the balancing act essential to guage such instances.
Singular and plural kinds have been merged, and verbs restored to their base (infinitive) kinds. Excessively broad phrases – similar to animation, scene, movie and motion – have been eliminated.
Thus 1,291 matters have been obtained (with the total record out there within the supply paper’s supplementary part).
Choose Net-Scraping
Subsequent, the researchers used the official YouTube API to hunt movies based mostly on the standards distilled from the 2024 dataset, in search of to acquire 500 movies for every subject. Moreover the requisite artistic commons license, every video needed to have a decision of 720p or greater, and needed to be shorter than 4 minutes.
On this manner 586,490 movies have been scraped from YouTube.
The authors in contrast the YouTube ID of the downloaded movies to a lot of well-liked datasets: OpenVid-1M; HD-VILA-100M; Intern-Vid; Koala-36M; LVD-2M; MiraData; Panda-70M; VidGen-1M; and WebVid-10M.
They discovered that only one,675 IDs (the aforementioned 0.29%) of the VideoUFO clips featured in these older collections, and it needs to be conceded that whereas the dataset comparability record just isn’t exhaustive, it does embrace all the largest and most influential gamers within the generative video scene.
Splits and Evaluation
The obtained movies have been subsequently segmented into a number of clips, in accordance with the methodology outlined within the Panda-70M paper cited above. Shot boundaries have been estimated, assemblies stitched, and the concatenated movies divided into single clips, with transient and detailed captions supplied.
Every information entry within the VideoUFO dataset encompasses a clip, an ID, begin and finish occasions, and a short and an in depth caption.
The transient captions have been dealt with by the Panda-70M technique, and the detailed video captions by Qwen2-VL-7B, alongside the rules established by Open-Sora-Plan. In instances the place clips didn’t efficiently embody the supposed goal idea, the detailed captions for every such clip have been fed into GPT-4o mini, with a view to confirm whether or not it was really a match for the subject. Although the authors would have most well-liked analysis through GPT-4o, this could have been too costly for thousands and thousands of video clips.
Video high quality evaluation was dealt with with six strategies from the VBench undertaking .
Comparisons
The authors repeated the subject extraction course of on the aforementioned prior datasets. For this, it was essential to semantically-match the derived classes of VideoUFO to the inevitably completely different classes within the different collections; it needs to be conceded that such processes provide solely approximated equal classes, and subsequently this can be too subjective a course of to vouchsafe empirical comparisons.
Nonetheless, within the picture under we see the outcomes the researchers obtained by this technique:
Comparability of the elemental attributes derived throughout VideoUFO and the prior datasets.
The researchers acknowledge that their evaluation relied on the present captions and descriptions supplied in every dataset. They admit that re-captioning older datasets utilizing the identical technique as VideoUFO might have supplied a extra direct comparability. Nonetheless, given the sheer quantity of information factors, their conclusion that this strategy can be prohibitively costly appears justified.
Technology
The authors developed a benchmark to guage text-to-video fashions’ efficiency on user-focused ideas, titled BenchUFO. This entailed choosing 791 nouns from the 1,291 distilled person matters in VideoUFO. For every chosen subject, ten textual content prompts from VidProM have been then randomly chosen.
Every immediate was handed by means of to a text-to-video mannequin, with the aforementioned Qwen2-VL-7B captioner used to guage the generated outcomes. With all generated movies thus captioned, SentenceTransformers was used to calculate cosine similarity for each the enter immediate and output (inferred) description in every case.
Schema for the BenchUFO course of.
The evaluated generative fashions have been: Mira; Present-1; LTX-Video; Open-Sora-Plan; Open Sora; TF-T2V; Mochi-1; HiGen; Pika; RepVideo; T2V-Zero; CogVideoX; Latte-1; Hunyuan Video; LaVie; and Pyramidal.
Moreover VideoUFO, MVDiT-VidGen and MVDit-OpenVid have been the choice coaching datasets.
The outcomes think about the Tenth-Fiftieth worst-performing and best-performing matters throughout the architectures and datasets.
Outcomes for the efficiency of public T2V fashions vs. the authors’ skilled fashions, on BenchUFO.
Right here the authors remark:
‘Present text-to-video fashions don’t constantly carry out properly throughout all user-focused matters. Particularly, there’s a rating distinction starting from 0.233 to 0.314 between the top-10 and low-10 matters. These fashions could not successfully perceive matters similar to “big squid”, “animal cell”, “Van Gogh”, and “historic Egyptian” as a consequence of inadequate coaching on such movies.
‘Present text-to-video fashions present a sure diploma of consistency of their best-performing matters. We uncover that the majority text-to-video fashions excel at producing movies on animal-related matters, similar to ‘seagull’, ‘panda’, ‘dolphin’, ‘camel’, and ‘owl’. We infer that that is partly as a consequence of a bias in the direction of animals in present video datasets.’
Conclusion
VideoUFO is an impressive providing if solely from the standpoint of recent information. If there was no error in evaluating and eliminating YouTube IDs, and if the dataset comprises a lot materials that’s new to the analysis scene, it’s a uncommon and probably worthwhile proposition.
The draw back is that one wants to offer credence to the core methodology; if you happen to do not consider that person demand ought to inform web-scraping formulation, you would be shopping for right into a dataset that comes with its personal units of troubling biases.
Additional, the utility of the distilled matters is determined by each the reliability of the distilling technique used (which is mostly hampered by price range constraints), and likewise the formulation strategies for the 2024 dataset that gives the supply materials.
That mentioned, VideoUFO actually deserves additional investigation – and it’s out there at Hugging Face.
* My substitution of the authors’ citations for hyperlinks.
First printed Wednesday, March 5, 2025
