The Violent Deconstruction Chain From 93,000 Characters to 1,029 Shots

Ninety-three thousand characters is the volume of a standard full-length novel, and 1,029 shots is the complete storyboard count of a two-hour-and-twenty-minute film. When you place these two numbers together and divide, you get a very intuitive proportional relationship: on average, 93,000 divided by 69 chapters gives roughly 1,348 characters per chapter, and each chapter splits into about 15 storyboards on average, with each storyboard carrying roughly 90 characters of original text content on average. But these 90 characters are not evenly distributed. Some storyboards contain only a single-sentence description, for example "turns and pushes the door" takes only twelve characters, while some grand action scenes require 300 characters of original text to support them. This compression in magnitude does not mean loss of information; rather, it means a fundamental transformation, converting continuously flowing novel narration into discrete, independently executable shot task units. To understand this, the key is to look at the internal logic of the funx.atctoscene function. This function is not a simple string splitter but a semantic parsing engine. It receives a dbvideo_article record, that is, the full text of one chapter, and then performs information extraction along four dimensions: character, location, action, and environment description. Character recognition relies on two signals. The first is noun matching: the system has a built-in basic name database containing common personal names and titles. The second is contextual pronoun resolution: for example, when "he" or "she" appears in a passage, the function traces back to the most recently appearing character name and associates it with the current action. Location recognition relies more on the co-occurrence of spatial prepositions and scene nouns; for example, verbs such as "arrive at," "walk toward," or "enter" often trigger a location update. When the function detects more than five consecutive lines of dialogue with no new action or scene change, it judges that the current scene has not yet ended and continues accumulating. But once an obvious spatial transition appears, for example the next paragraph opening with "at the same time" or "on the other side," it forcibly splits a new storyboard. Each time a storyboard is generated, the system automatically inserts a record into dbvideo_process and links in the identified character primary key and location primary key, forming a complete executable instruction. Taking a specific battle scene in the Five Elements Dragon Flag chapter as an example, the original text reads: "Lin Xuan turned and leaped onto the city wall, the long blade in his hand tracing a flash of silver light as it slashed straight down at the man in black below. The man in black raised his sword to block, sparks flying amid the clang of metal, the two figures crossing several times. Seizing the moment, Lin Xuan reversed his hand and drove the blade into the other's ribs. The man in black grunted, staggered backward, the torches on the city wall extinguished by the gust of wind, and darkness fell all around." These four sentences of original text are about 120 characters. During parsing, the function first identifies the characters Lin Xuan and the man in black, the location as the city wall, the actions including "leaped onto," "slash," "block," "cross," "thrust into," and "retreat," and the environment description including "clang of metal," "sparks flying," "torches extinguished," and "darkness." Based on action density and temporal continuity, the function judges this passage to be one complete shot and generates one process record, whose action field is automatically assembled as "Lin Xuan leaps onto the city wall, swings his blade at the man in black, who raises his sword to block, sparks fly, blades cross, then Lin Xuan reverses and thrusts into the other's ribs, the man in black retreats into the shadows, the torches go out and darkness surrounds." This shot instruction is then sent to arcpictask to generate first-frame and last-frame image prompts, and after image generation, it is driven into the finished film by the Jimeng video model. The precision of this deconstruction directly determines the quality of subsequent image generation and video generation. If the function mistakenly split the battle scene above into two storyboards, for example the first storyboard containing only the leap onto the wall and the slash, and the second only the block and the thrust, then the generated videos would become two disjointed action segments lacking a sense of continuity in between. To reduce this risk, the function sets a scene continuity threshold: when neither the character nor the location changes, no matter how dialogue and action switch, it prioritizes merging them into one storyboard. Only when either the character or the location changes, or when an explicit narrative transition marker appears in the original text, does it trigger a forced split. Therefore, the 69 chapters of the entire book ultimately generate 1,029 shots, not a crude number based on paragraph count. The average of fifteen storyboards per chapter reflects the normal distribution of narrative rhythm in a novel, but there are also extreme chapters. For example, the opening ambush scene in Five Elements Dragon Flag splits a single chapter into twenty-seven storyboards, because that chapter frequently switches between different characters' perspectives and multiple combat positions, while some later chapters with lower narrative density have only eight storyboards. Deconstruction efficiency data is equally important. Processing the full text of all 69 chapters of "The First Avenue," the system completed the entire storyboard deconstruction on three standard-configuration computers in a total of about four hours and eighteen minutes, with an average deconstruction time of about 3.7 minutes per chapter. The largest chapter, the prologue of Five Elements Dragon Flag, contains about 2,100 characters and took 6.2 minutes, while the smallest chapter, a transitional passage, has only 900 characters and took 1.8 minutes. This speed means the deconstruction process is not the time bottleneck of the entire project; compared to the dozens of hours spent in the subsequent image generation and video generation stages, it is merely a minor episode. The runtime of the function is mainly determined by two factors: the number of times the external large model's context window is invoked and the number of tokens returned each time. atctoscene does not send the entire chapter text to the model in one shot. Instead, it splits the chapter into several text blocks, each of roughly 400 to 600 characters, and sends them block by block. After each text block returns its structured extraction result, it merges them into the complete storyboard sequence. This block-wise processing both avoids the instability of long-text generation in large models and allows progress tracking at the function-call level. The function also has a built-in interesting design: hierarchical validation of extraction results. Each time a storyboard is generated, the system checks whether the storyboard contains at least one character or one location. If both are empty, it judges the storyboard as invalid and merges it back into the previous storyboard. During the deconstruction of the entire book, such invalid storyboards were merged a total of thirty-seven times, accounting for about 3.6 percent, indicating that most sentences can be effectively assigned to some scene. In the final deconstruction results, there are 912 storyboards containing both characters and locations, and 117 empty-shot storyboards containing only locations without characters. This set of data is consistent with common sense in film production, where empty shots without people are typically used for setting the scene, transitions, or expressing emotion. In the measured specifications of the finished "The First Avenue," the voiced proportion is 87.2 percent, which exactly corresponds to the ratio of the 912 character-bearing storyboards to the total number of shots. It can be seen that the output quality of the deconstruction function directly determines the dialogue and sound-effect coverage rate in the finished film. In industrialized film workflows, this number is usually the result of subjective selection by the director at the editing table, but here it is a byproduct of automatic deconstruction. Ultimately, the core value of this violent deconstruction is to convert ambiguous literary language into precise, enumerable, and re-runnable structured data items. After every novel text undergoes this function, it is no longer a string of sentences but a database snapshot containing a character table, a location table, and a storyboard table. You can generate images, generate video, or replace characters for each storyboard individually, and you can also re-run the entire book as a whole while maintaining consistency of results. This enumerability allows the creative process of a film to be decomposed by a computer into thousands of atomic tasks, each with a unique status and output, thereby supporting multi-machine parallelism, resume-from-breakpoint, and post-production replacement. When you watch the complete film generated from 1,029 shots and then look back at the 93,000-character original text, your perspective has shifted from continuous linguistic appreciation to discrete engineering-structural analysis. This is precisely the greatest methodological contribution of the Yuncao Academy approach: it makes the conversion from text to image no longer a mysterious subjective creative act, but a single standardized procedure within an industrialized workflow.